In [ ]:
# STEP 1: GEE EXTRACTION
import ee
import geemap
import os
from google.colab import drive
# 1. SETUP
drive.mount('/content/drive', force_remount=True)
MY_PROJECT_ID = '[REDACTED_FOR_SECURITY]'
try:
ee.Initialize(project=MY_PROJECT_ID)
print(f"GEE Initialized: {MY_PROJECT_ID}")
except:
ee.Authenticate()
ee.Initialize(project=MY_PROJECT_ID)
# CONFIG
WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'
SAVE_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
if not os.path.exists(SAVE_DIR): os.makedirs(SAVE_DIR)
START_DATE = '2023-10-01'
END_DATE = '2024-04-30'
TOTAL_POINTS = 10000
SPLIT = TOTAL_POINTS // 2
# --- 2. BALANCED SAMPLING ---
print("Generating 50/50 Balanced Samples...")
wheat_mask = ee.Image(WHEAT_MASK_ASSET)
bounds = wheat_mask.geometry()
# A. Wheat (Class 1)
wheat_points = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=42) \
.filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())) \
.limit(SPLIT).map(lambda f: f.set('class', 1))
# B. Non-Wheat (Class 0)
non_wheat_points = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=99) \
.filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())) \
.limit(SPLIT).map(lambda f: f.set('class', 0))
points_for_analysis = wheat_points.merge(non_wheat_points)
print(f"Total Points: {points_for_analysis.size().getInfo()}")
# --- 3. SATELLITE PROCESSING (SAFE LEE FILTER) ---
def apply_lee_filter_safe(image):
"""
Safe Lee Filter: Uses a CONSTANT (0.004) for noise variance.
This prevents the 'Dictionary.get' crash on defective images.
"""
def lee_single(b):
img_band = image.select(b)
# Local Statistics (3x3 Kernel)
weights = ee.Kernel.square(3)
mean = img_band.reduceNeighborhood(ee.Reducer.mean(), weights)
variance = img_band.reduceNeighborhood(ee.Reducer.variance(), weights)
# CRITICAL FIX: Use Constant 0.004 (Standard for S1 IW)
overall_var_img = ee.Image.constant(0.004)
k = variance.divide(variance.add(overall_var_img))
return mean.add(k.multiply(img_band.subtract(mean))).rename(b)
return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)
def maskS2clouds(image):
qa = image.select('QA60')
mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))
return image.updateMask(mask).select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']) \
.copyProperties(image, ["system:time_start"])
# --- 4. ROBUST COLLECTIONS ---
s1_col = ee.ImageCollection('COPERNICUS/S1_GRD') \
.filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.eq('instrumentMode', 'IW')) \
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING')) \
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) \
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH')) \
.map(apply_lee_filter_safe).select(['VV', 'VH'])
s2_col = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \
.filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)) \
.map(maskS2clouds)
# --- 5. DAILY COMPOSITES ---
def make_daily_composite(collection, check_bands):
dates = collection.aggregate_array('system:time_start') \
.map(lambda t: ee.Date(t).format('YYYY-MM-dd')).distinct()
def create_composite(date_str):
d = ee.Date(date_str)
# Bounds filter optimization
daily = collection.filterDate(d, d.advance(1, 'day')) \
.filter(ee.Filter.bounds(points_for_analysis)) \
.median().set('system:time_start', d.millis())
return daily
col = ee.ImageCollection.fromImages(dates.map(create_composite))
for b in check_bands:
col = col.filter(ee.Filter.listContains('system:band_names', b))
return col
daily_s1 = make_daily_composite(s1_col, ['VV', 'VH'])
daily_s2 = make_daily_composite(s2_col, ['B2'])
# --- 6. EXPORT TASKS (Split & Robust) ---
def extract_robust(collection, band_names, prefix):
def sample_image(img):
samples = img.select(band_names).reduceRegions(
collection=points_for_analysis,
reducer=ee.Reducer.first(),
scale=10,
tileScale=16
)
return samples.map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))
return collection.map(sample_image).flatten()
# Export S1
print("Submitting Sentinel-1 Task...")
s1_flat = extract_robust(daily_s1, ['VV', 'VH'], 'S1')
s1_flat = s1_flat.select(['.*'], None, False)
task1 = ee.batch.Export.table.toDrive(
collection=s1_flat, description=f'Wheat_S1_{START_DATE}_{END_DATE}',
folder='LSTM_Wheat_Results', fileNamePrefix='Wheat_S1_Raw', fileFormat='CSV'
)
task1.start()
# Export S2
print("Submitting Sentinel-2 Task...")
s2_flat = extract_robust(daily_s2, ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12'], 'S2')
s2_flat = s2_flat.select(['.*'], None, False)
task2 = ee.batch.Export.table.toDrive(
collection=s2_flat, description=f'Wheat_S2_{START_DATE}_{END_DATE}',
folder='LSTM_Wheat_Results', fileNamePrefix='Wheat_S2_Raw', fileFormat='CSV'
)
task2.start()
print("Tasks submitted. Proceed to Step 2.")
Mounted at /content/drive GEE Initialized: local-dialect-484618-b9 Generating 50/50 Balanced Samples... Total Points: 10000 Submitting Sentinel-1 Task... Submitting Sentinel-2 Task... Tasks submitted. Proceed to Step 2.
In [ ]:
# PART 1: GEE EXTRACTION
import ee
import geemap
import os
from google.colab import drive
# 1. SETUP
drive.mount('/content/drive', force_remount=True)
try:
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
except:
ee.Authenticate()
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
# CANCEL STUCK TASKS
print("--- CANCELLING STUCK TASKS ---")
tasks = ee.batch.Task.list()
for t in tasks:
if t.state == 'READY' and 'Wheat' in t.config['description']:
print(f"Cancelling: {t.config['description']}")
ee.data.cancelTask(t.id)
# CONFIG
WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'
START_DATE = '2023-10-01'
END_DATE = '2024-04-30'
BATCHES = [
{'name': 'Batch_A', 'seed': 42}, # First 5,000 points
{'name': 'Batch_B', 'seed': 123} # Second 5,000 points
]
POINTS_PER_BATCH = 5000
SPLIT = POINTS_PER_BATCH // 2
# --- FUNCTIONS ---
def apply_lee_filter_safe(image):
def lee_single(b):
img_band = image.select(b)
weights = ee.Kernel.square(3)
mean = img_band.reduceNeighborhood(ee.Reducer.mean(), weights)
variance = img_band.reduceNeighborhood(ee.Reducer.variance(), weights)
overall_var_img = ee.Image.constant(0.004)
k = variance.divide(variance.add(overall_var_img))
return mean.add(k.multiply(img_band.subtract(mean))).rename(b)
return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)
def maskS2clouds(image):
qa = image.select('QA60')
mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))
return image.updateMask(mask).select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']) \
.copyProperties(image, ["system:time_start"])
def make_daily_composite(collection, points, check_bands):
dates = collection.aggregate_array('system:time_start') \
.map(lambda t: ee.Date(t).format('YYYY-MM-dd')).distinct()
def create_composite(date_str):
d = ee.Date(date_str)
daily = collection.filterDate(d, d.advance(1, 'day')) \
.filter(ee.Filter.bounds(points)) \
.median().set('system:time_start', d.millis())
return daily
col = ee.ImageCollection.fromImages(dates.map(create_composite))
for b in check_bands: col = col.filter(ee.Filter.listContains('system:band_names', b))
return col
def extract_robust(collection, points, band_names, prefix):
def sample_image(img):
samples = img.select(band_names).reduceRegions(
collection=points,
reducer=ee.Reducer.first(),
scale=10,
tileScale=16
)
return samples.map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))
return collection.map(sample_image).flatten()
# --- EXECUTE BATCHES ---
wheat_mask = ee.Image(WHEAT_MASK_ASSET)
bounds = wheat_mask.geometry()
for batch in BATCHES:
batch_name = batch['name']
seed = batch['seed']
print(f"\n PREPARING {batch_name} (5,000 Points)...")
# 1. Generate Points
wheat_pts = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=seed) \
.filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())) \
.limit(SPLIT).map(lambda f: f.set('class', 1))
non_wheat_pts = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=seed+99) \
.filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())) \
.limit(SPLIT).map(lambda f: f.set('class', 0))
points = wheat_pts.merge(non_wheat_pts)
# 2. Prepare Collections
s1_col = ee.ImageCollection('COPERNICUS/S1_GRD') \
.filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.eq('instrumentMode', 'IW')) \
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING')) \
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) \
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH')) \
.map(apply_lee_filter_safe).select(['VV', 'VH'])
s2_col = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \
.filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)) \
.map(maskS2clouds)
daily_s1 = make_daily_composite(s1_col, points, ['VV', 'VH'])
daily_s2 = make_daily_composite(s2_col, points, ['B2'])
# 3. Submit Tasks
# S1 Export
s1_flat = extract_robust(daily_s1, points, ['VV', 'VH'], 'S1')
s1_flat = s1_flat.select(['.*'], None, False)
task1 = ee.batch.Export.table.toDrive(
collection=s1_flat, description=f'Wheat_S1_{batch_name}',
folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S1_{batch_name}', fileFormat='CSV'
)
task1.start()
# S2 Export
s2_flat = extract_robust(daily_s2, points, ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12'], 'S2')
s2_flat = s2_flat.select(['.*'], None, False)
task2 = ee.batch.Export.table.toDrive(
collection=s2_flat, description=f'Wheat_S2_{batch_name}',
folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S2_{batch_name}', fileFormat='CSV'
)
task2.start()
print(f" Tasks for {batch_name} submitted.")
print("\n All 4 tasks submitted (2 batches x 2 sensors).")
print("These smaller tasks should start MUCH faster.")
print("Restart the Watchdog script to monitor them.")
Mounted at /content/drive --- CANCELLING STUCK TASKS --- Cancelling: Wheat_S2_2023-10-01_2024-04-30 Cancelling: Wheat_S1_2023-10-01_2024-04-30 Cancelling: Wheat_S2_2023-10-01_2024-04-30 Cancelling: Wheat_S1_2023-10-01_2024-04-30 Cancelling: Wheat_S2_2023-10-01_2024-04-30 Cancelling: Wheat_S1_2023-10-01_2024-04-30 🚀 PREPARING Batch_A (5,000 Points)... Tasks for Batch_A submitted. 🚀 PREPARING Batch_B (5,000 Points)... Tasks for Batch_B submitted. ✅ All 4 tasks submitted (2 batches x 2 sensors). These smaller tasks should start MUCH faster. Restart the Watchdog script to monitor them.
In [ ]:
import time
import os
import ee
FOLDER = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("--- DEEP SCAN TASK MONITOR (FIXED) ---")
while True:
try:
tasks = ee.batch.Task.list()
except Exception as e:
print(f"Connection glitch, retrying... {e}")
time.sleep(10)
continue
# Filter for our specific tasks
my_tasks = [t for t in tasks[:15] if 'Wheat' in t.config['description']]
print(f"\nTime: {time.strftime('%H:%M:%S')}")
print(f"{'TASK NAME':<25} | {'STATUS':<12} | {'DRIVE FILE'}")
print("-" * 60)
all_completed = True
any_failed = False
for t in my_tasks:
name = t.config['description']
status = t.state
# CRASH PROOF FIX:
# We assume filename matches the Task Description (which we set up to be true)
fname = name + '.csv'
fpath = FOLDER + fname
# Check size if exists
if os.path.exists(fpath):
size_mb = os.path.getsize(fpath) / (1024 * 1024)
file_status = f"YES ({size_mb:.1f} MB)"
else:
file_status = "NO"
print(f"{name:<25} | {status:<12} | {file_status}")
if status != 'COMPLETED':
all_completed = False
if status == 'FAILED':
any_failed = True
# Safe error retrieval
err = t.status().get('error_message', 'Unknown Error')
print(f" Error: {err}")
# SUCCESS CHECK
# We need at least 4 COMPLETED tasks (S1_Batch_A, S2_Batch_A, S1_Batch_B, S2_Batch_B)
completed_count = sum(1 for t in my_tasks if t.state == 'COMPLETED')
if completed_count >= 4 and all_completed:
print("\n SUCCESS! All batches are finished.")
print("You can now proceed to Part 2 (The Merge).")
break
if any_failed:
print("\n FAILURE DETECTED. Please check the error message above.")
break
time.sleep(60)
--- DEEP SCAN TASK MONITOR (FIXED) --- Time: 17:39:58 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | READY | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:40:58 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | READY | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:41:58 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | READY | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:42:58 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | READY | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:43:58 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | READY | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:44:58 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | READY | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:45:59 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:46:59 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:47:59 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:48:59 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:49:59 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:50:59 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:52:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:53:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:54:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:55:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:56:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:57:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:58:00 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 17:59:01 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | READY | NO Wheat_S1_Batch_A | RUNNING | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:00:01 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:01:01 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:02:01 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:03:01 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:04:01 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:05:02 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:06:02 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:07:02 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:08:02 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:09:02 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:10:02 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:11:03 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:12:03 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:13:03 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:14:03 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:15:03 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:16:03 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:17:04 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:18:04 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:19:04 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:20:04 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:21:05 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:22:05 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:23:05 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | READY | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:24:05 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:25:05 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:26:05 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:27:06 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO
WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance. WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.
Time: 18:28:06 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:29:07 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:30:07 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:31:07 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:32:07 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:33:07 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | READY | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:34:07 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:35:08 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:36:08 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:37:08 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:38:08 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:39:08 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:40:08 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:41:09 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:42:09 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:43:09 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:44:09 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:45:09 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:46:09 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:47:10 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:48:10 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:49:10 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:50:10 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:51:10 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:52:11 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:53:11 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:54:11 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:55:11 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:56:11 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | NO Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:57:11 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:58:12 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 18:59:12 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:00:12 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:01:12 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:02:13 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:03:13 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:04:13 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:05:13 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:06:13 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:07:13 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:08:14 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:09:14 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:10:14 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:11:14 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:12:14 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:13:14 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:14:15 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:15:15 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:16:15 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:17:15 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:18:15 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:19:15 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:20:16 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:21:16 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:22:16 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:23:16 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:24:16 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO
WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance. WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.
Time: 19:25:17 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:26:17 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:27:17 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:28:18 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:29:18 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:30:18 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:31:18 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:32:18 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:33:18 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:34:19 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:35:19 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:36:19 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:37:19 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:38:19 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:39:19 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:40:20 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:41:20 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:42:20 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:43:20 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:44:20 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:45:20 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:46:21 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:47:21 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:48:21 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:49:21 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:50:21 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:51:21 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:52:22 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:53:22 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:54:22 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:55:22 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:56:22 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:57:22 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:58:23 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 19:59:23 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:00:23 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:01:23 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:02:23 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:03:23 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:04:24 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:05:24 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:06:24 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:07:24 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:08:24 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:09:25 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:10:25 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:11:25 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:12:25 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:13:25 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:14:25 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:15:26 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:16:26 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:17:26 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Time: 20:18:26 TASK NAME | STATUS | DRIVE FILE ------------------------------------------------------------ Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB) Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB) Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB) Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) /tmp/ipython-input-1927954896.py in <cell line: 0>() 63 break 64 ---> 65 time.sleep(60) KeyboardInterrupt:
In [ ]:
import pandas as pd
import os
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("--- CSV INSPECTOR ---")
try:
# Load just the first 5 rows of Batch A (Radar)
test_path = INPUT_DIR + 'Wheat_S1_Batch_A.csv'
df_test = pd.read_csv(test_path, nrows=5)
print(f"\nFile: {test_path}")
print("Found Columns:")
print(df_test.columns.tolist())
print("\nFirst 2 Rows of Data:")
print(df_test.head(2))
except FileNotFoundError:
print(f"Could not find {test_path}")
except Exception as e:
print(f" Error reading file: {e}")
--- CSV INSPECTOR ---
File: /content/drive/MyDrive/LSTM_Wheat_Results/Wheat_S1_Batch_A.csv
Found Columns:
['system:index', 'class', 'date', 'sensor_type', '.geo']
First 2 Rows of Data:
system:index class date sensor_type \
0 0_1_0 1 2023-10-02 S1
1 0_1_1 1 2023-10-02 S1
.geo
0 {"type":"MultiPoint","coordinates":[]}
1 {"type":"MultiPoint","coordinates":[]}
In [ ]:
# PART 2: MERGE BATCHES & PROCESS
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
from scipy.signal import savgol_filter
import os
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
COMMON_START = pd.Timestamp('2023-10-01')
COMMON_END = pd.Timestamp('2024-04-30')
# --- 1. GENERATE TARGET DATES (Exact Logic from Old Code) ---
TARGET_DATES = []
curr = COMMON_START
while curr <= COMMON_END:
if curr.day == 1 or curr.day == 16:
TARGET_DATES.append(curr)
curr += pd.Timedelta(days=1)
TARGET_DAYS_INT = [(t - COMMON_START).days for t in TARGET_DATES]
print(f"Target Time Steps: {len(TARGET_DATES)}") # Should be 14
# --- 2. HELPER: SPIKE REMOVAL (Exact Logic) ---
def remove_spikes(series, threshold=0.4):
"""
Removes sudden jumps > 0.4 in Optical data.
"""
diff = series.diff().abs()
mask = (diff > threshold)
series_clean = series.copy()
series_clean[mask] = np.nan
return series_clean
print("3. Loading & Merging Batches...")
try:
# Load all 4 batch files
s1_a = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_A.csv')
s2_a = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_A.csv')
s1_b = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_B.csv')
s2_b = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_B.csv')
print(" All 4 batch files loaded.")
# Stack Batches (A + B)
df_s1 = pd.concat([s1_a, s1_b], ignore_index=True)
df_s2 = pd.concat([s2_a, s2_b], ignore_index=True)
# Clean IDs (Exact Logic)
df_s1['unique_id'] = df_s1['system:index'].apply(lambda x: x.split('_')[-1])
df_s2['unique_id'] = df_s2['system:index'].apply(lambda x: x.split('_')[-1])
# Master Merge
df = pd.concat([df_s1, df_s2], ignore_index=True)
df['date'] = pd.to_datetime(df['date'])
except FileNotFoundError:
print(" ERROR: Files missing. Please wait for GEE tasks to finish.")
raise
# --- 3. METADATA & CONVERSION ---
meta_df = df[['unique_id', 'class']].drop_duplicates()
# dB to Linear Conversion (Exact Logic)
print(" Converting dB to Linear...")
for b in ['VV', 'VH']:
mask = (df['sensor_type'] == 'S1') & (df[b].notna())
df.loc[mask, b] = 10**(df.loc[mask, b]/10.0)
# --- 4. MAIN PROCESSING LOOP ---
X_list, y_list = [], []
grouped = df.groupby('unique_id')
print(f"4. Processing {len(grouped)} points...")
count = 0
for pid, group in grouped:
# Get Label
try: label = meta_df[meta_df['unique_id'] == pid]['class'].iloc[0]
except: continue
# 80% Rule (Exact Logic)
if len(group[group['sensor_type'] == 'S2']) < 5: continue
p_mat, valid = [], True
bands = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']
for band in bands:
ts = group[['date', band]].dropna().sort_values('date')
# Spike Removal (Optical Only)
if band.startswith('B'):
ts[band] = remove_spikes(ts[band], threshold=0.4)
ts = ts.dropna()
# Check Length
if len(ts) < 2:
valid = False; break
# Interpolation (Exact Logic)
days = (ts['date'] - COMMON_START).dt.days.values
vals = ts[band].values
f = interp1d(days, vals, kind='linear', fill_value='extrapolate')
res = f(TARGET_DAYS_INT)
# SavGol Filter (Exact Logic)
window = 5
if len(res) < window: window = 3
try: smoothed = savgol_filter(res, window, 2)
except: smoothed = res
p_mat.append(smoothed)
if valid:
X_list.append(np.array(p_mat).T)
y_list.append(label)
count += 1
if count % 1000 == 0: print(f" Processed {count}...")
# --- 5. SAVE ---
X_data, y_data = np.array(X_list), np.array(y_list)
np.save(INPUT_DIR + 'X_wheat.npy', X_data)
np.save(INPUT_DIR + 'y_wheat.npy', y_data)
print(f"\n PROCESSING COMPLETE.")
print(f"Final Data Shape: {X_data.shape}")
print(f"Labels Shape: {y_data.shape}")
In [ ]:
# PART 3: LSTM TRAINING
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader, random_split
import numpy as np
import matplotlib.pyplot as plt
import os
# LOAD DATA
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
# HYPERPARAMETERS
INPUT_DIM = 12
HIDDEN_DIM = 64
LAYERS = 2
EPOCHS = 200
BATCH_SIZE = 32
LR = 0.001
# 1. DATASET
class WheatDataset(Dataset):
def __init__(self, X, y):
self.X = torch.tensor(X, dtype=torch.float32)
self.y = torch.tensor(y, dtype=torch.float32).unsqueeze(1)
def __len__(self): return len(self.X)
def __getitem__(self, i): return self.X[i], self.y[i]
dataset = WheatDataset(X, y)
train_len = int(0.8 * len(dataset))
train_set, val_set = random_split(dataset, [train_len, len(dataset)-train_len])
train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
val_loader = DataLoader(val_set, batch_size=BATCH_SIZE)
# 2. MODEL
class WheatLSTM(nn.Module):
def __init__(self):
super(WheatLSTM, self).__init__()
# batch_first=True -> (Batch, Seq, Feat)
self.lstm = nn.LSTM(INPUT_DIM, HIDDEN_DIM, LAYERS, batch_first=True, dropout=0.2)
self.fc = nn.Linear(HIDDEN_DIM, 1) # Output 1 score
self.sigmoid = nn.Sigmoid()
def forward(self, x):
# x shape: (Batch, 14, 12)
# out shape: (Batch, 14, 64)
out, _ = self.lstm(x)
# We only want the LAST time step (the end of the season)
last_step = out[:, -1, :]
prediction = self.sigmoid(self.fc(last_step))
return prediction
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = WheatLSTM().to(device)
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=LR)
# 3. TRAIN LOOP
train_losses = []
val_accuracies = []
# Ensure checkpoint folder exists (optional specific folder)
CKPT_DIR = INPUT_DIR + 'checkpoints/'
if not os.path.exists(CKPT_DIR): os.makedirs(CKPT_DIR)
print("Starting Training...")
for epoch in range(EPOCHS):
model.train()
batch_loss = 0
for X_batch, y_batch in train_loader:
X_batch, y_batch = X_batch.to(device), y_batch.to(device)
optimizer.zero_grad()
y_pred = model(X_batch)
loss = criterion(y_pred, y_batch)
loss.backward()
optimizer.step()
batch_loss += loss.item()
# Validation
model.eval()
correct = 0
total = 0
with torch.no_grad():
for X_val, y_val in val_loader:
X_val, y_val = X_val.to(device), y_val.to(device)
preds = model(X_val)
predicted_class = (preds > 0.5).float()
correct += (predicted_class == y_val).sum().item()
total += y_val.size(0)
val_acc = correct / total
train_losses.append(batch_loss/len(train_loader))
val_accuracies.append(val_acc)
# Print Progress
if (epoch + 1) % 10 == 0:
print(f"Epoch {epoch+1}: Loss={batch_loss/len(train_loader):.4f} | Val Acc={val_acc*100:.2f}%")
if (epoch + 1) % 5 == 0:
ckpt_path = CKPT_DIR + f'wheat_lstm_epoch_{epoch+1}.pth'
torch.save({
'epoch': epoch + 1,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': batch_loss/len(train_loader),
'val_acc': val_acc
}, ckpt_path)
print(f" -> Checkpoint saved: {ckpt_path}")
# 4. PLOT
plt.plot(train_losses)
plt.title("Training Loss")
plt.show()
# SAVE FINAL MODEL
torch.save(model.state_dict(), INPUT_DIR + 'wheat_lstm_final.pth')
print("Final Model Saved.")
In [ ]:
# PART 1: SCIENTIFIC GEE EXTRACTION
import ee
import os
from google.colab import drive
# 1. SETUP
drive.mount('/content/drive', force_remount=True)
try:
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
except:
ee.Authenticate()
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
# CONFIG
WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'
START_DATE = '2023-10-01'
END_DATE = '2024-04-30'
BATCHES = [{'name': 'Batch_A', 'seed': 42}, {'name': 'Batch_B', 'seed': 123}]
POINTS_PER_BATCH = 5000
SPLIT = POINTS_PER_BATCH // 2
# --- 2. SCIENTIFIC FUNCTIONS ----
def apply_lee_filter_safe(image):
def lee_single(b):
img_band = image.select(b)
mean = img_band.reduceNeighborhood(ee.Reducer.mean(), ee.Kernel.square(3))
variance = img_band.reduceNeighborhood(ee.Reducer.variance(), ee.Kernel.square(3))
overall_var_img = ee.Image.constant(0.004) # Standard scientific constant
k = variance.divide(variance.add(overall_var_img))
return mean.add(k.multiply(img_band.subtract(mean))).rename(b)
return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)
def maskS2clouds(image):
qa = image.select('QA60')
mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))
return image.updateMask(mask).select(['B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']) \
.copyProperties(image, ["system:time_start"])
def make_semimonthly_composites(collection):
"""Scientific Aggregation Logic: 15-day median binned steps."""
start = ee.Date(START_DATE)
end = ee.Date(END_DATE)
n_months = end.difference(start, 'month').round()
def create_steps(m):
m_start = start.advance(m, 'month')
mid_month = m_start.advance(15, 'day')
next_month = m_start.advance(1, 'month')
# 1st-15th Median
img1 = collection.filterDate(m_start, mid_month).median() \
.set('system:time_start', m_start.millis())
# 16th-End Median
img2 = collection.filterDate(mid_month, next_month).median() \
.set('system:time_start', mid_month.millis())
return ee.List([img1, img2])
steps = ee.List.sequence(0, n_months.subtract(1)).map(create_steps).flatten()
return ee.ImageCollection.fromImages(steps)
# --- 3. EXECUTION ---
wheat_mask = ee.Image(WHEAT_MASK_ASSET)
bounds = wheat_mask.geometry()
for batch in BATCHES:
b_name = batch['name']
print(f"\n SUBMITTING {b_name} (Strict Scientific Mode)...")
# BALANCED SAMPLING (Wheat & Non-Wheat)
w_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']) \
.filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 1))
n_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']+99) \
.filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 0))
points = w_pts.merge(n_pts)
# Collections
s1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.eq('instrumentMode', 'IW')).map(apply_lee_filter_safe)
s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)).map(maskS2clouds)
# Server-Side Compositing (Refrence Gate)
agg_s1 = make_semimonthly_composites(s1.select(['VV', 'VH']))
agg_s2 = make_semimonthly_composites(s2)
def extract(img, prefix, bands):
return img.select(bands).reduceRegions(collection=points, reducer=ee.Reducer.first(), scale=10, tileScale=16) \
.map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))
# Strict Exports (Forcing Columns)
ee.batch.Export.table.toDrive(
collection=agg_s1.map(lambda i: extract(i, 'S1', ['VV', 'VH'])).flatten(),
description=f'Wheat_S1_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S1_{b_name}',
fileFormat='CSV', selectors=['system:index', 'class', 'date', 'sensor_type', 'VV', 'VH']
).start()
ee.batch.Export.table.toDrive(
collection=agg_s2.map(lambda i: extract(i, 'S2', ['B2','B3','B4','B5','B6','B7','B8','B8A', 'B11', 'B12'])).flatten(),
description=f'Wheat_S2_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S2_{b_name}',
fileFormat='CSV', selectors=['system:index', 'class', 'date', 'sensor_type', 'B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']
).start()
print("Tasks submitted. Wait for COMPLETED status.")
In [ ]:
# PART 2: THE REFINEMENT GATE
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
from scipy.signal import savgol_filter
from tqdm.notebook import tqdm
import os
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
COMMON_START = pd.Timestamp('2023-10-01')
# Exact 14 timestamps (1st and 15th)
TARGET_DATES = [COMMON_START + pd.Timedelta(days=d) for d in range(213) if (COMMON_START + pd.Timedelta(days=d)).day in [1, 15]]
TARGET_DAYS_INT = [(t - COMMON_START).days for t in TARGET_DATES]
def remove_spikes(series):
# Reference Spike Logic (threshold 0.4)
diff = series.diff().abs()
series[diff > 0.4] = np.nan
return series
print("1. Loading Data (0 to NaN Trick)...")
# na_values=0 ensures that zeros are treated as "No Data" during interpolation
s1_a = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_A.csv', na_values=0)
s2_a = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_A.csv', na_values=0)
s1_b = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_B.csv', na_values=0)
s2_b = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_B.csv', na_values=0)
df = pd.concat([s1_a, s2_a, s1_b, s2_b], ignore_index=True)
df['unique_id'] = df['system:index'].apply(lambda x: x.split('_')[-1])
df['date'] = pd.to_datetime(df['date'])
# Conversion Gate (Linear Scale)
for b in ['VV', 'VH']:
mask = (df['sensor_type'] == 'S1') & (df[b].notna())
df.loc[mask, b] = 10**(df.loc[mask, b]/10.0)
X_list, y_list = [], []
grouped = df.groupby('unique_id')
print(f"2. Applying 80% Filter & SavGol Smoothing...")
for pid, group in tqdm(grouped, total=len(grouped)):
label = group['class'].iloc[0]
if len(group[group['sensor_type'] == 'S2'].dropna(subset=['B2'])) < 5: continue
p_mat, valid = [], True
bands = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']
for band in bands:
ts = group[['date', band]].dropna().sort_values('date')
if band.startswith('B'): ts[band] = remove_spikes(ts[band])
ts = ts.dropna()
if len(ts) < 2:
valid = False; break
# Interpolation Gate
f = interp1d((ts['date'] - COMMON_START).dt.days.values, ts[band].values, kind='linear', fill_value='extrapolate')
res = f(TARGET_DAYS_INT)
# REFINEMENT GATE: Savitzky-Golay (Window 5, Poly 2)
try: smoothed = savgol_filter(res, 5, 2)
except: smoothed = res
p_mat.append(smoothed)
if valid:
X_list.append(np.array(p_mat).T)
y_list.append(label)
X_data, y_data = np.array(X_list), np.array(y_list)
np.save(INPUT_DIR + 'X_wheat.npy', X_data)
np.save(INPUT_DIR + 'y_wheat.npy', y_data)
print(f" SUCCESS: Dataset saved. Shape: {X_data.shape}")
In [ ]:
# PART 3: LSTM TRAINING
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os
# 1. LOAD DATA
DATA_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
X = np.load(DATA_DIR + 'X_wheat.npy')
y = np.load(DATA_DIR + 'y_wheat.npy')
print(f"Loaded Data: {X.shape}, Labels: {y.shape}")
# 2. STANDARDIZATION (CRITICAL FOR LSTM)
# We flatten to (N*T, F) to fit the scaler, then reshape back
N, T, F = X.shape
X_flat = X.reshape(-1, F)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_flat).reshape(N, T, F)
# 3. TRAIN-TEST SPLIT
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
# 4. DEFINE ARCHITECTURE
model = Sequential([
# Layer 1: Captures initial temporal patterns
LSTM(128, input_shape=(T, F), return_sequences=True),
BatchNormalization(),
Dropout(0.2),
# Layer 2: Deep temporal reasoning
LSTM(64),
BatchNormalization(),
Dropout(0.2),
# Decision Head
Dense(32, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
# 5. CHECKPOINT & CALLBACKS
checkpoint_path = DATA_DIR + "checkpoints/wheat_model_epoch_{epoch:02d}.h5"
if not os.path.exists(DATA_DIR + "checkpoints/"):
os.makedirs(DATA_DIR + "checkpoints/")
# Save every 5 epochs
checkpoint_callback = ModelCheckpoint(
filepath=checkpoint_path,
save_weights_only=False,
monitor='val_accuracy',
mode='max',
save_freq=5 * (len(X_train) // 32)
)
# Early stopping to save time if model converges early
early_stop = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)
# 6. TRAIN
print("\n Starting Training for 200 Epochs...")
history = model.fit(
X_train, y_train,
validation_data=(X_test, y_test),
epochs=200,
batch_size=32,
callbacks=[checkpoint_callback, early_stop],
verbose=1
)
# 7. SAVE FINAL MODEL
model.save(DATA_DIR + 'wheat_lstm_final.h5')
print(f"\n Training Complete. Final model saved to: {DATA_DIR}wheat_lstm_final.h5")
---------------------------------------------------------NEW updated CODE--------------------------------------------------------------------------------------
In [1]:
# PART 1: SCIENTIFIC GEE EXTRACTION
import ee
import os
from google.colab import drive
# 1. SETUP
drive.mount('/content/drive', force_remount=True)
try:
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
except:
ee.Authenticate()
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
# CONFIG
WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'
START_DATE = '2023-10-01'
END_DATE = '2024-04-30'
BATCHES = [{'name': 'Batch_A', 'seed': 42}, {'name': 'Batch_B', 'seed': 123}]
POINTS_PER_BATCH = 5000
SPLIT = POINTS_PER_BATCH // 2
def apply_lee_filter_safe(image):
def lee_single(b):
img_band = image.select(b)
mean = img_band.reduceNeighborhood(ee.Reducer.mean(), ee.Kernel.square(3))
variance = img_band.reduceNeighborhood(ee.Reducer.variance(), ee.Kernel.square(3))
overall_var_img = ee.Image.constant(0.004)
k = variance.divide(variance.add(overall_var_img))
return mean.add(k.multiply(img_band.subtract(mean))).rename(b)
return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)
def maskS2clouds(image):
qa = image.select('QA60')
mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))
return image.updateMask(mask).select(['B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']) \
.copyProperties(image, ["system:time_start"])
def make_semimonthly_composites(collection):
"""
Aggregation: 15-day median binned steps.
"""
start = ee.Date(START_DATE)
end = ee.Date(END_DATE)
n_months = end.difference(start, 'month').round()
def create_steps(m):
m_start = start.advance(m, 'month')
mid_month = m_start.advance(15, 'day')
next_month = m_start.advance(1, 'month')
# 1st-15th Median
img1 = collection.filterDate(m_start, mid_month).median() \
.set('system:time_start', m_start.millis())
# 16th-End Median
img2 = collection.filterDate(mid_month, next_month).median() \
.set('system:time_start', mid_month.millis())
return ee.List([img1, img2])
steps = ee.List.sequence(0, n_months.subtract(1)).map(create_steps).flatten()
return ee.ImageCollection.fromImages(steps)
# --- 3. EXECUTION ---
wheat_mask = ee.Image(WHEAT_MASK_ASSET)
bounds = wheat_mask.geometry()
# Cancel old tasks to keep queue clean
tasks = ee.batch.Task.list()
for t in tasks:
if t.state in ['READY', 'RUNNING'] and 'Wheat' in t.config['description']:
ee.data.cancelTask(t.id)
for batch in BATCHES:
b_name = batch['name']
print(f"\n SUBMITTING {b_name} (Strict Scientific Mode)...")
# BALANCED SAMPLING
w_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']) \
.filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 1))
n_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']+99) \
.filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 0))
points = w_pts.merge(n_pts)
# Collections
s1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.eq('instrumentMode', 'IW')).map(apply_lee_filter_safe)
s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterDate(START_DATE, END_DATE).filterBounds(bounds) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)).map(maskS2clouds)
# Server-Side Compositing (The "Median" Step)
agg_s1 = make_semimonthly_composites(s1.select(['VV', 'VH']))
agg_s2 = make_semimonthly_composites(s2)
def extract(img, prefix, bands):
# Uses tileScale=16
return img.select(bands).reduceRegions(collection=points, reducer=ee.Reducer.first(), scale=10, tileScale=16) \
.map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))
# Strict Exports (With Selectors)
ee.batch.Export.table.toDrive(
collection=agg_s1.map(lambda i: extract(i, 'S1', ['VV', 'VH'])).flatten(),
description=f'Wheat_S1_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S1_{b_name}',
fileFormat='CSV',
selectors=['system:index', 'class', 'date', 'sensor_type', 'VV', 'VH']
).start()
ee.batch.Export.table.toDrive(
collection=agg_s2.map(lambda i: extract(i, 'S2', ['B2','B3','B4','B5','B6','B7','B8','B8A', 'B11', 'B12'])).flatten(),
description=f'Wheat_S2_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S2_{b_name}',
fileFormat='CSV',
selectors=['system:index', 'class', 'date', 'sensor_type', 'B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']
).start()
print("Tasks submitted.")
Mounted at /content/drive SUBMITTING Batch_A (Strict Scientific Mode)... SUBMITTING Batch_B (Strict Scientific Mode)... Tasks submitted.
In [3]:
import time
import os
import ee
from google.colab import drive
# Ensure Drive is accessible for file checking
if not os.path.exists('/content/drive'):
drive.mount('/content/drive')
FOLDER = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("--- DEEP SCAN TASK MONITOR (SCIENTIFIC MODE) ---")
while True:
try:
# Fetch latest task list
tasks = ee.batch.Task.list()
except Exception as e:
print(f"Connection glitch ({e}), retrying in 10s...")
time.sleep(10)
continue
# We take the top 20 to ensure we catch all active ones
my_tasks = [t for t in tasks[:20] if 'Wheat' in t.config['description']]
# Sort them so S1/S2 and Batch A/B appear in a consistent order
my_tasks.sort(key=lambda t: t.config['description'])
print(f"\nTime: {time.strftime('%H:%M:%S')}")
print(f"{'TASK NAME':<30} | {'STATUS':<12} | {'DRIVE FILE (MB)'}")
print("-" * 70)
all_completed = True
any_failed = False
completed_count = 0
for t in my_tasks:
name = t.config['description']
status = t.state
# Construct expected filename matches the export code
# Logic: description 'Wheat_S1_Batch_A' -> filename 'Wheat_S1_Batch_A.csv'
fname = name + '.csv'
fpath = FOLDER + fname
# Check if file exists in Drive and get size
if os.path.exists(fpath):
size_mb = os.path.getsize(fpath) / (1024 * 1024)
file_status = f"YES ({size_mb:.1f} MB)"
else:
file_status = "NO"
print(f"{name:<30} | {status:<12} | {file_status}")
# Logic Checks
if status == 'COMPLETED':
completed_count += 1
else:
all_completed = False
if status == 'FAILED':
any_failed = True
err = t.status().get('error_message', 'Unknown Error')
print(f" ERROR: {err}")
if completed_count >= 4 and all_completed:
print("\n SUCCESS! All 4 scientific batches are finished.")
print("You can now proceed to Part 2 (The Refinement/Merge).")
break
if any_failed:
print("\n FAILURE DETECTED. Please check the error message above.")
break
# Refresh every 60 seconds
time.sleep(60)
--- DEEP SCAN TASK MONITOR (SCIENTIFIC MODE) --- Time: 05:32:02 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | RUNNING | NO Wheat_S1_Batch_B | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | READY | NO Wheat_S2_Batch_B | COMPLETED | NO Time: 05:33:02 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | RUNNING | NO Wheat_S2_Batch_B | COMPLETED | NO Time: 05:34:02 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | RUNNING | NO Wheat_S2_Batch_B | COMPLETED | NO Time: 05:35:02 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | RUNNING | NO Wheat_S2_Batch_B | COMPLETED | NO Time: 05:36:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:37:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:38:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:39:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:40:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:41:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:42:03 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:43:04 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:44:04 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:45:04 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | RUNNING | NO Wheat_S2_Batch_A | COMPLETED | NO Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:46:04 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:47:04 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:48:05 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:49:05 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:50:05 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:51:05 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:52:05 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:53:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:54:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:55:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:56:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:57:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:58:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 05:59:06 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:00:07 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:01:07 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:02:07 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:03:07 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:04:08 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:05:08 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:06:08 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:07:08 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:08:08 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Time: 06:09:08 TASK NAME | STATUS | DRIVE FILE (MB) ---------------------------------------------------------------------- Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB) Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB) Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) /tmp/ipython-input-516124977.py in <cell line: 0>() 76 77 # Refresh every 60 seconds ---> 78 time.sleep(60) KeyboardInterrupt:
In [4]:
import os
import pandas as pd
import glob
# CONFIG
FOLDER = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print(f" INSPECTING FOLDER: {FOLDER}\n")
# 1. FIND FILES
files = sorted(glob.glob(FOLDER + "*.csv"))
if not files:
print(" NO FILES FOUND. Please wait for GEE tasks to finish.")
else:
print(f" Found {len(files)} CSV files.\n")
print(f"{'FILENAME':<40} | {'SIZE (MB)':<10} | {'STATUS'} | {'COLUMNS CHECK'}")
print("-" * 110)
for f in files:
# Get Size
size_mb = os.path.getsize(f) / (1024 * 1024)
name = os.path.basename(f)
# Check Status based on size
status = " OK"
if size_mb < 0.01: status = " EMPTY"
elif size_mb < 5: status = " SMALL"
# Peek Inside (Check Columns)
try:
df = pd.read_csv(f, nrows=2)
cols = df.columns.tolist()
# Check for Critical Data Columns
if 'S1' in name:
has_data = 'VV' in cols and 'VH' in cols
col_status = " S1 Data Found" if has_data else " MISSING VV/VH"
elif 'S2' in name:
has_data = 'B2' in cols
col_status = " S2 Data Found" if has_data else " MISSING BANDS"
else:
col_status = " Unknown Type"
except Exception as e:
col_status = f" Error reading: {str(e)}"
print(f"{name:<40} | {size_mb:<10.2f} | {status:<6} | {col_status}")
print("\n--------------------------------------------------------------------------------------------------------------")
print("GUIDE:")
print("1. Sizes: S1 files should be ~10-15 MB. S2 files should be ~40-60 MB.")
print("2. Columns: You MUST see ' Data Found'. If you see ' MISSING', the selectors failed.")
🔍 INSPECTING FOLDER: /content/drive/MyDrive/LSTM_Wheat_Results/ ✅ Found 4 CSV files. FILENAME | SIZE (MB) | STATUS | COLUMNS CHECK -------------------------------------------------------------------------------------------------------------- Wheat_S1_Batch_A.csv | 4.27 | ⚠️ SMALL | ✅ S1 Data Found Wheat_S1_Batch_B.csv | 4.27 | ⚠️ SMALL | ✅ S1 Data Found Wheat_S2_Batch_A.csv | 5.89 | ✅ OK | ✅ S2 Data Found Wheat_S2_Batch_B.csv | 5.89 | ✅ OK | ✅ S2 Data Found -------------------------------------------------------------------------------------------------------------- GUIDE: 1. Sizes: S1 files should be ~10-15 MB. S2 files should be ~40-60 MB. 2. Columns: You MUST see '✅ Data Found'. If you see '❌ MISSING', the selectors failed.
In [21]:
# PART 2: THE REFINEMENT GATE
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
from scipy.signal import savgol_filter
from tqdm.notebook import tqdm
import os
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
COMMON_START = pd.Timestamp('2023-10-01')
TARGET_DATES = [COMMON_START + pd.Timedelta(days=d) for d in range(213) if (COMMON_START + pd.Timedelta(days=d)).day in [1, 15]]
TARGET_DAYS_INT = [(t - COMMON_START).days for t in TARGET_DATES]
def remove_spikes(series):
s = series.copy()
diff = s.diff().abs()
# Now that data is normalized (0-1), 0.4 is a valid threshold
s.loc[diff > 0.4] = np.nan
return s
print("1. Loading Data & Normalizing...")
try:
s1_a = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_A.csv'); s1_a['batch'] = 'A'
s2_a = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_A.csv'); s2_a['batch'] = 'A'
s1_b = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_B.csv'); s1_b['batch'] = 'B'
s2_b = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_B.csv'); s2_b['batch'] = 'B'
df = pd.concat([s1_a, s2_a, s1_b, s2_b], ignore_index=True)
# 1. Clean 0s (Nodata)
sensor_cols = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']
cols_exist = [c for c in sensor_cols if c in df.columns]
df[cols_exist] = df[cols_exist].replace(0, np.nan)
# 2. THE FIX: NORMALIZE OPTICAL DATA (0-10000 -> 0.0-1.0)
# This makes the values compatible with the Spike Filter (0.4)
optical_cols = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']
opt_exist = [c for c in optical_cols if c in df.columns]
df[opt_exist] = df[opt_exist] / 10000.0
# 3. ID Override (Keep merging logic)
df['id_body'] = df['system:index'].apply(lambda x: '_'.join(str(x).split('_')[1:]))
df['unique_id'] = df['batch'] + '_' + df['id_body']
# 4. Class Fix
df['class'] = df['class'].fillna(-1).astype(int)
df['date'] = pd.to_datetime(df['date'])
# 5. Radar Linear Conversion
for b in ['VV', 'VH']:
mask = (df['sensor_type'] == 'S1') & (df[b].notna())
df.loc[mask, b] = 10**(df.loc[mask, b]/10.0)
print(" Data Loaded & Normalized correctly.")
except Exception as e:
raise Exception(f" Setup Failed: {e}")
X_list, y_list = [], []
grouped = df.groupby('unique_id')
print(f"2. Processing {len(grouped)} Unique Farms...")
for pid, group in tqdm(grouped, total=len(grouped)):
try:
label_val = group['class'].max()
if label_val == -1: continue
except: continue
valid_s2 = group[group['sensor_type'] == 'S2'].dropna(subset=['B2'])
if len(valid_s2) < 3:
continue
p_mat, valid = [], True
bands = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']
for band in bands:
ts = group[['date', band]].dropna().groupby('date').mean().sort_index()
ts_vals = ts[band]
ts_dates = ts.index
# Spike Filter (Now safe because data is 0-1)
if band.startswith('B'):
ts_vals = remove_spikes(ts_vals)
mask = ~np.isnan(ts_vals)
ts_vals = ts_vals[mask]; ts_dates = ts_dates[mask]
if len(ts_vals) < 2:
valid = False; break
try:
f = interp1d((ts_dates - COMMON_START).days, ts_vals, kind='linear', fill_value='extrapolate')
res = f(TARGET_DAYS_INT)
except:
valid = False; break
try:
window = 5 if len(res) >= 5 else 3
smoothed = savgol_filter(res, window, 2)
except:
smoothed = res
p_mat.append(smoothed)
if valid:
X_list.append(np.array(p_mat).T)
y_list.append(label_val)
X_data = np.array(X_list)
y_data = np.array(y_list)
if len(X_data) > 0:
np.save(INPUT_DIR + 'X_wheat.npy', X_data)
np.save(INPUT_DIR + 'y_wheat.npy', y_data)
print(f"\n SUCCESS: Dataset saved. Shape: {X_data.shape}")
else:
print("\n FAILURE: Still 0 points. Logic mismatch persists.")
1. Loading Data & Normalizing... Data Loaded & Normalized correctly. 2. Processing 10000 Unique Farms...
0%| | 0/10000 [00:00<?, ?it/s]
SUCCESS: Dataset saved. Shape: (10000, 14, 12)
In [17]:
import ee
import os
from google.colab import drive
# 1. Mount Drive
drive.mount('/content/drive', force_remount=True)
# 2. Initialize Earth Engine
try:
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
except:
ee.Authenticate()
ee.Initialize(project='[REDACTED_FOR_SECURITY]')
print(" Connection Restored. You can now run the Band Survivor Check.")
Mounted at /content/drive ✅ Connection Restored. You can now run the Band Survivor Check.
In [22]:
# PART 3: THE INTELLIGENCE GATE
import numpy as np
import tensorflow as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from google.colab import drive
import matplotlib.pyplot as plt
# 1. Re-Connect to Drive (Since Runtime Restarted)
drive.mount('/content/drive')
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("1. Loading Processed Data...")
try:
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
print(f" Data Loaded. Shape: {X.shape}")
except FileNotFoundError:
raise Exception(" Data not found. Make sure Part 2 finished successfully.")
# 2. Split Data (80% Train, 20% Test)
# We use a random state for reproducibility
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(f" Training on {len(X_train)} farms.")
print(f" Testing on {len(X_test)} farms.")
# 3. Build the LSTM Brain
# This architecture is optimized for 14-step Time Series
model = Sequential([
# Layer 1: The Input Gate (Reads the sequence)
LSTM(64, return_sequences=True, input_shape=(14, 12)),
BatchNormalization(),
Dropout(0.3), # Prevents memorization (overfitting)
# Layer 2: The Deep Processing (Finds hidden patterns)
LSTM(32, return_sequences=False),
BatchNormalization(),
Dropout(0.3),
# Layer 3: The Decision Maker (Wheat vs Non-Wheat)
Dense(16, activation='relu'),
Dense(1, activation='sigmoid') # Output: Probability (0.0 to 1.0)
])
# 4. Compile (The Learning Strategy)
model.compile(optimizer=Adam(learning_rate=0.001),
loss='binary_crossentropy',
metrics=['accuracy'])
print("\n2. Starting Training (GPU Recommended)...")
# 5. Train
history = model.fit(
X_train, y_train,
epochs=50, # How many times to loop through data
batch_size=32, # Process 32 farms at a time
validation_data=(X_test, y_test),
verbose=1
)
# 6. Save the Trained Brain
model.save(INPUT_DIR + 'Wheat_LSTM_Model.h5')
print(f"\nSUCCESS: Model Trained & Saved to Drive.")
# 7. Visualization: Did it learn?
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Test Accuracy')
plt.title('Model Intelligence (Accuracy)')
plt.xlabel('Epochs')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Test Loss')
plt.title('Model Mistakes (Loss)')
plt.xlabel('Epochs')
plt.legend()
plt.show()
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
1. Loading Processed Data...
Data Loaded. Shape: (10000, 14, 12)
Training on 8000 farms.
Testing on 2000 farms.
/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
2. Starting Training (GPU Recommended)... Epoch 1/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 9s 16ms/step - accuracy: 0.4954 - loss: 0.7387 - val_accuracy: 0.5015 - val_loss: 0.6943 Epoch 2/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4901 - loss: 0.7147 - val_accuracy: 0.4720 - val_loss: 0.7002 Epoch 3/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 18ms/step - accuracy: 0.5044 - loss: 0.7005 - val_accuracy: 0.5050 - val_loss: 0.6983 Epoch 4/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.4892 - loss: 0.7003 - val_accuracy: 0.4970 - val_loss: 0.6986 Epoch 5/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5151 - loss: 0.6963 - val_accuracy: 0.4975 - val_loss: 0.6951 Epoch 6/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.4957 - loss: 0.6978 - val_accuracy: 0.5025 - val_loss: 0.6977 Epoch 7/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.4927 - loss: 0.6966 - val_accuracy: 0.4975 - val_loss: 0.6953 Epoch 8/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.5038 - loss: 0.6974 - val_accuracy: 0.5075 - val_loss: 0.6942 Epoch 9/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 18ms/step - accuracy: 0.4969 - loss: 0.6946 - val_accuracy: 0.5045 - val_loss: 0.6935 Epoch 10/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 18ms/step - accuracy: 0.5189 - loss: 0.6948 - val_accuracy: 0.4860 - val_loss: 0.6947 Epoch 11/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4948 - loss: 0.6956 - val_accuracy: 0.5030 - val_loss: 0.6949 Epoch 12/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5028 - loss: 0.6954 - val_accuracy: 0.5010 - val_loss: 0.6971 Epoch 13/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 18ms/step - accuracy: 0.5033 - loss: 0.6947 - val_accuracy: 0.4930 - val_loss: 0.6939 Epoch 14/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.4992 - loss: 0.6953 - val_accuracy: 0.4920 - val_loss: 0.6977 Epoch 15/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5068 - loss: 0.6932 - val_accuracy: 0.5040 - val_loss: 0.6945 Epoch 16/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.5095 - loss: 0.6934 - val_accuracy: 0.4925 - val_loss: 0.6976 Epoch 17/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 15ms/step - accuracy: 0.5160 - loss: 0.6920 - val_accuracy: 0.4895 - val_loss: 0.6947 Epoch 18/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5104 - loss: 0.6926 - val_accuracy: 0.4960 - val_loss: 0.6958 Epoch 19/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 18ms/step - accuracy: 0.4963 - loss: 0.6948 - val_accuracy: 0.4705 - val_loss: 0.6951 Epoch 20/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5141 - loss: 0.6936 - val_accuracy: 0.4855 - val_loss: 0.6944 Epoch 21/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5152 - loss: 0.6935 - val_accuracy: 0.4925 - val_loss: 0.6932 Epoch 22/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 18ms/step - accuracy: 0.4988 - loss: 0.6941 - val_accuracy: 0.5045 - val_loss: 0.6931 Epoch 23/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5121 - loss: 0.6939 - val_accuracy: 0.4935 - val_loss: 0.6944 Epoch 24/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5090 - loss: 0.6935 - val_accuracy: 0.4975 - val_loss: 0.7012 Epoch 25/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 18ms/step - accuracy: 0.4837 - loss: 0.6942 - val_accuracy: 0.4950 - val_loss: 0.6946 Epoch 26/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.5058 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6935 Epoch 27/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5116 - loss: 0.6935 - val_accuracy: 0.5115 - val_loss: 0.6933 Epoch 28/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.4921 - loss: 0.6936 - val_accuracy: 0.5180 - val_loss: 0.6928 Epoch 29/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.5060 - loss: 0.6935 - val_accuracy: 0.5030 - val_loss: 0.6938 Epoch 30/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4852 - loss: 0.6942 - val_accuracy: 0.5330 - val_loss: 0.6928 Epoch 31/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5022 - loss: 0.6930 - val_accuracy: 0.4850 - val_loss: 0.6948 Epoch 32/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5120 - loss: 0.6933 - val_accuracy: 0.5110 - val_loss: 0.6945 Epoch 33/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4907 - loss: 0.6933 - val_accuracy: 0.4880 - val_loss: 0.6933 Epoch 34/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5043 - loss: 0.6933 - val_accuracy: 0.4900 - val_loss: 0.6938 Epoch 35/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.5039 - loss: 0.6935 - val_accuracy: 0.4955 - val_loss: 0.6932 Epoch 36/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 18ms/step - accuracy: 0.4970 - loss: 0.6933 - val_accuracy: 0.4900 - val_loss: 0.6943 Epoch 37/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4927 - loss: 0.6935 - val_accuracy: 0.4910 - val_loss: 0.6932 Epoch 38/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4939 - loss: 0.6935 - val_accuracy: 0.5090 - val_loss: 0.6933 Epoch 39/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 21ms/step - accuracy: 0.5126 - loss: 0.6929 - val_accuracy: 0.4910 - val_loss: 0.6938 Epoch 40/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5048 - loss: 0.6928 - val_accuracy: 0.4980 - val_loss: 0.6936 Epoch 41/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5050 - val_loss: 0.6932 Epoch 42/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 18ms/step - accuracy: 0.5155 - loss: 0.6932 - val_accuracy: 0.4990 - val_loss: 0.6937 Epoch 43/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.4991 - loss: 0.6932 - val_accuracy: 0.4995 - val_loss: 0.6950 Epoch 44/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5006 - loss: 0.6926 - val_accuracy: 0.4920 - val_loss: 0.6960 Epoch 45/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 17ms/step - accuracy: 0.4963 - loss: 0.6933 - val_accuracy: 0.4935 - val_loss: 0.6933 Epoch 46/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5158 - loss: 0.6926 - val_accuracy: 0.4980 - val_loss: 0.6933 Epoch 47/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4928 - loss: 0.6936 - val_accuracy: 0.4830 - val_loss: 0.6942 Epoch 48/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.5091 - loss: 0.6927 - val_accuracy: 0.4960 - val_loss: 0.6938 Epoch 49/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 16ms/step - accuracy: 0.5033 - loss: 0.6931 - val_accuracy: 0.4825 - val_loss: 0.6962 Epoch 50/50 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.4985 - loss: 0.6934 - val_accuracy: 0.5075 - val_loss: 0.6931
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
SUCCESS: Model Trained & Saved to Drive.
In [23]:
# ==========================================
# PART 4: DATA HEALTH CHECK (POST-PROCESSING)
# ==========================================
import numpy as np
import matplotlib.pyplot as plt
import os
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("--- DATA QUALITY ASSURANCE REPORT ---")
try:
# 1. Load the Saved Data
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
print(f" Files Loaded.")
print(f" X Shape: {X.shape} (Farms, Dates, Features)")
print(f" y Shape: {y.shape} (Labels)")
# 2. Check for "Silent Killers" (NaNs or Infinite values)
nan_count = np.isnan(X).sum()
inf_count = np.isinf(X).sum()
if nan_count == 0 and inf_count == 0:
print("CLEANLINESS CHECK PASSED: No NaNs or Infinite values found.")
else:
print(f" WARNING: Found {nan_count} NaNs and {inf_count} Infs!")
# 3. Value Range Check (Are the numbers physically realistic?)
# We expect Optical to be 0.0 - 1.0 (Reflectance)
# We expect Radar (Linear) to be roughly 0.0 - 0.5
print("\n[STATISTICAL CHECK]")
print(f" Global Min Value: {X.min():.4f}")
print(f" Global Max Value: {X.max():.4f}")
print(f" Mean Value: {X.mean():.4f}")
if X.max() > 100:
print(" WARNING: Max value is huge (>100). Did Normalization fail?")
elif X.max() < 0.001:
print(" WARNING: Max value is tiny (<0.001). Signal might be lost.")
else:
print(" RANGE CHECK PASSED: Values look like normalized satellite data.")
# 4. Class Balance Check
unique, counts = np.unique(y, return_counts=True)
balance = dict(zip(unique, counts))
print("\n[CLASS BALANCE]")
print(f" Labels found: {balance}")
if len(balance) < 2:
print(" CRITICAL FAILURE: Only one class exists! Model cannot learn.")
else:
print(" BALANCE CHECK PASSED: Both Wheat and Non-Wheat exist.")
# 5. Visual Sanity Check (The "Eye Test")
# Plot a random Wheat farm vs a Non-Wheat farm
print("\n[VISUAL INSPECTION]")
print(" Plotting random samples to check for 'Crop Curves'...")
wheat_indices = np.where(y == 1)[0]
non_wheat_indices = np.where(y == 0)[0]
if len(wheat_indices) > 0 and len(non_wheat_indices) > 0:
# Pick random ones
w_idx = np.random.choice(wheat_indices)
n_idx = np.random.choice(non_wheat_indices)
plt.figure(figsize=(14, 5))
# Plot Wheat (Band 2 vs Band 8 - Red vs NIR usually shows growth)
plt.subplot(1, 2, 1)
plt.plot(X[w_idx, :, 2], label='B2 (Blue)', marker='o') # Index 2 is B2
plt.plot(X[w_idx, :, 8], label='B8 (NIR)', marker='o', color='green') # Index 8 is B8
plt.title(f'Sample WHEAT Farm (ID {w_idx})')
plt.xlabel('Time Steps (Fortnights)')
plt.ylabel('Reflectance (0-1)')
plt.legend()
plt.grid(True)
# Plot Non-Wheat
plt.subplot(1, 2, 2)
plt.plot(X[n_idx, :, 2], label='B2 (Blue)', marker='o')
plt.plot(X[n_idx, :, 8], label='B8 (NIR)', marker='o', color='green')
plt.title(f'Sample NON-WHEAT Farm (ID {n_idx})')
plt.xlabel('Time Steps (Fortnights)')
plt.ylabel('Reflectance (0-1)')
plt.legend()
plt.grid(True)
plt.show()
print(" (Look at the plots: Wheat should show a 'Green Bump' in B8. Non-Wheat is often flat or random.)")
except Exception as e:
print(f" Error during QA: {e}")
--- DATA QUALITY ASSURANCE REPORT ---
✅ Files Loaded.
X Shape: (10000, 14, 12) (Farms, Dates, Features)
y Shape: (10000,) (Labels)
✅ CLEANLINESS CHECK PASSED: No NaNs or Infinite values found.
[STATISTICAL CHECK]
Global Min Value: -0.6523
Global Max Value: 30.3852
Mean Value: 0.1692
✅ RANGE CHECK PASSED: Values look like normalized satellite data.
[CLASS BALANCE]
Labels found: {np.int64(0): np.int64(5000), np.int64(1): np.int64(5000)}
✅ BALANCE CHECK PASSED: Both Wheat and Non-Wheat exist.
[VISUAL INSPECTION]
Plotting random samples to check for 'Crop Curves'...
(Look at the plots: Wheat should show a 'Green Bump' in B8. Non-Wheat is often flat or random.)
In [24]:
# PART 3: THE INTELLIGENCE GATE
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import time
import os
# 1. Force CPU Mode
tf.config.set_visible_devices([], 'GPU')
print(" CPU MODE ACTIVE. Using Smart Callbacks to save time.")
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("1. Loading Processed Data...")
try:
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
print(f" Data Loaded. Shape: {X.shape}")
except FileNotFoundError:
raise Exception(" Data not found.")
# 2. Split Data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 3. Build the Researcher-Grade LSTM (Same robust architecture)
model = Sequential([
LSTM(64, return_sequences=True, input_shape=(14, 12)),
BatchNormalization(),
Dropout(0.3),
LSTM(32, return_sequences=False),
BatchNormalization(),
Dropout(0.3),
Dense(16, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer=Adam(learning_rate=0.001),
loss='binary_crossentropy',
metrics=['accuracy'])
# --- THE SMART TRAINING TOOLS ---
callbacks = [
# 1. Early Stopping: Stop if no improvement for 25 epochs
EarlyStopping(monitor='val_loss', patience=25, verbose=1, restore_best_weights=True),
# 2. Model Checkpoint: ALWAYS save the best model found so far
ModelCheckpoint(
filepath=INPUT_DIR + 'Best_Wheat_LSTM.h5',
monitor='val_loss',
save_best_only=True, # Only overwrite if this epoch is better
verbose=1
)
]
print("\n2. Starting Smart Training...")
print(" - Max Epochs: 200")
print(" - Stop if no progress for: 25 epochs")
print(" - Auto-Saving best model to Drive")
start_time = time.time()
history = model.fit(
X_train, y_train,
epochs=200, # Cap at 200 (1000 is too risky on CPU)
batch_size=32,
validation_data=(X_test, y_test),
callbacks=callbacks, # Activate the tools
verbose=1
)
end_time = time.time()
duration = (end_time - start_time) / 60
print(f"\n TRAINING COMPLETE in {duration:.1f} minutes.")
print(f" Best Model saved as: {INPUT_DIR}Best_Wheat_LSTM.h5")
# 4. Visualization
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Test Accuracy')
plt.title('Accuracy Curve')
plt.legend(); plt.grid(True)
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Test Loss')
plt.title('Loss Curve')
plt.legend(); plt.grid(True)
plt.show()
CPU MODE ACTIVE. Using Smart Callbacks to save time. 1. Loading Processed Data... Data Loaded. Shape: (10000, 14, 12) 2. Starting Smart Training... - Max Epochs: 200 - Stop if no progress for: 25 epochs - Auto-Saving best model to Drive Epoch 1/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.5077 - loss: 0.7456 Epoch 1: val_loss improved from inf to 0.69432, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 11s 26ms/step - accuracy: 0.5077 - loss: 0.7455 - val_accuracy: 0.4850 - val_loss: 0.6943 Epoch 2/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5107 - loss: 0.7033 Epoch 2: val_loss did not improve from 0.69432 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 22ms/step - accuracy: 0.5107 - loss: 0.7033 - val_accuracy: 0.5260 - val_loss: 0.6945 Epoch 3/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4993 - loss: 0.7028 Epoch 3: val_loss did not improve from 0.69432 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4994 - loss: 0.7028 - val_accuracy: 0.4855 - val_loss: 0.7000 Epoch 4/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5040 - loss: 0.6972 Epoch 4: val_loss improved from 0.69432 to 0.69287, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.5040 - loss: 0.6972 - val_accuracy: 0.5150 - val_loss: 0.6929 Epoch 5/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.5045 - loss: 0.6980 Epoch 5: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 18ms/step - accuracy: 0.5045 - loss: 0.6980 - val_accuracy: 0.4960 - val_loss: 0.6942 Epoch 6/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.4952 - loss: 0.6969 Epoch 6: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4952 - loss: 0.6969 - val_accuracy: 0.5115 - val_loss: 0.6943 Epoch 7/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.4919 - loss: 0.6969 Epoch 7: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4919 - loss: 0.6969 - val_accuracy: 0.4835 - val_loss: 0.6945 Epoch 8/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5055 - loss: 0.6969 Epoch 8: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.5055 - loss: 0.6969 - val_accuracy: 0.5195 - val_loss: 0.6934 Epoch 9/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.5174 - loss: 0.6957 Epoch 9: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5173 - loss: 0.6957 - val_accuracy: 0.4885 - val_loss: 0.6952 Epoch 10/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5088 - loss: 0.6946 Epoch 10: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5086 - loss: 0.6946 - val_accuracy: 0.4895 - val_loss: 0.6946 Epoch 11/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5046 - loss: 0.6952 Epoch 11: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5046 - loss: 0.6952 - val_accuracy: 0.4995 - val_loss: 0.6944 Epoch 12/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5018 - loss: 0.6960 Epoch 12: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 18ms/step - accuracy: 0.5018 - loss: 0.6960 - val_accuracy: 0.4945 - val_loss: 0.6931 Epoch 13/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5000 - loss: 0.6957 Epoch 13: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.4999 - loss: 0.6957 - val_accuracy: 0.4940 - val_loss: 0.6933 Epoch 14/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5001 - loss: 0.6943 Epoch 14: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5001 - loss: 0.6944 - val_accuracy: 0.5040 - val_loss: 0.6944 Epoch 15/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.5181 - loss: 0.6924 Epoch 15: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 18ms/step - accuracy: 0.5180 - loss: 0.6924 - val_accuracy: 0.4965 - val_loss: 0.6930 Epoch 16/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.5093 - loss: 0.6940 Epoch 16: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.5093 - loss: 0.6940 - val_accuracy: 0.5045 - val_loss: 0.6942 Epoch 17/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4952 - loss: 0.6937 Epoch 17: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4952 - loss: 0.6937 - val_accuracy: 0.4975 - val_loss: 0.6941 Epoch 18/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4948 - loss: 0.6960 Epoch 18: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.4950 - loss: 0.6960 - val_accuracy: 0.5010 - val_loss: 0.6944 Epoch 19/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.4913 - loss: 0.6949 Epoch 19: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 20ms/step - accuracy: 0.4914 - loss: 0.6949 - val_accuracy: 0.4980 - val_loss: 0.6940 Epoch 20/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5112 - loss: 0.6936 Epoch 20: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5110 - loss: 0.6936 - val_accuracy: 0.5025 - val_loss: 0.6930 Epoch 21/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4973 - loss: 0.6936 Epoch 21: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4973 - loss: 0.6936 - val_accuracy: 0.5160 - val_loss: 0.6930 Epoch 22/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5070 - loss: 0.6926 Epoch 22: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5069 - loss: 0.6926 - val_accuracy: 0.4990 - val_loss: 0.6940 Epoch 23/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5014 - loss: 0.6933 Epoch 23: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5013 - loss: 0.6933 - val_accuracy: 0.4970 - val_loss: 0.6931 Epoch 24/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5088 - loss: 0.6935 Epoch 24: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5087 - loss: 0.6935 - val_accuracy: 0.4925 - val_loss: 0.6936 Epoch 25/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.5089 - loss: 0.6931 Epoch 25: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 18ms/step - accuracy: 0.5089 - loss: 0.6931 - val_accuracy: 0.4975 - val_loss: 0.6942 Epoch 26/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4964 - loss: 0.6950 Epoch 26: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4964 - loss: 0.6950 - val_accuracy: 0.4975 - val_loss: 0.6944 Epoch 27/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5150 - loss: 0.6932 Epoch 27: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5150 - loss: 0.6932 - val_accuracy: 0.4900 - val_loss: 0.6954 Epoch 28/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.4949 - loss: 0.6944 Epoch 28: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 18ms/step - accuracy: 0.4949 - loss: 0.6944 - val_accuracy: 0.5020 - val_loss: 0.6948 Epoch 29/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.4867 - loss: 0.6950 Epoch 29: val_loss did not improve from 0.69287 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.4868 - loss: 0.6950 - val_accuracy: 0.5025 - val_loss: 0.6948 Epoch 29: early stopping Restoring model weights from the end of the best epoch: 4. TRAINING COMPLETE in 2.2 minutes. Best Model saved as: /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM.h5
In [25]:
# PART 3: THE INTELLIGENCE GATE
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import time
import os
# 1. Force CPU Mode
tf.config.set_visible_devices([], 'GPU')
print(" CPU MODE ACTIVE. Training with StandardScaler.")
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
print("1. Loading Data...")
try:
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
print(f" Data Loaded. Shape: {X.shape}")
except FileNotFoundError:
raise Exception("Data not found.")
# --- THE FIX: STANDARD SCALING ---
# We must reshape to 2D to scale, then reshape back to 3D for LSTM
print("2. Normalizing Features (StandardScaler)...")
N, T, F = X.shape # (Farms, Time, Features)
# Flatten: (10000 farms * 14 dates, 12 features)
X_flat = X.reshape(N * T, F)
# Scale: Make all bands have Mean=0, Std=1
scaler = StandardScaler()
X_scaled_flat = scaler.fit_transform(X_flat)
# Reshape back: (10000, 14, 12)
X_scaled = X_scaled_flat.reshape(N, T, F)
print(f" Scaling complete. Radar and Optical are now balanced.")
# ---------------------------------
# 3. Split Data
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
# 4. Build Model (Same Architecture)
model = Sequential([
LSTM(64, return_sequences=True, input_shape=(14, 12)),
BatchNormalization(),
Dropout(0.3),
LSTM(32, return_sequences=False),
BatchNormalization(),
Dropout(0.3),
Dense(16, activation='relu'),
Dense(1, activation='sigmoid')
])
# Reduced Learning Rate slightly to prevent "bouncing"
model.compile(optimizer=Adam(learning_rate=0.0005),
loss='binary_crossentropy',
metrics=['accuracy'])
callbacks = [
EarlyStopping(monitor='val_loss', patience=40, verbose=1, restore_best_weights=True),
ModelCheckpoint(
filepath=INPUT_DIR + 'Best_Wheat_LSTM_Scaled.h5',
monitor='val_loss',
save_best_only=True,
verbose=1
)
]
print("\n3. Starting Training (Scaled)...")
start_time = time.time()
history = model.fit(
X_train, y_train,
epochs=200, # 200 is plenty if data is scaled correctly
batch_size=32,
validation_data=(X_test, y_test),
callbacks=callbacks,
verbose=1
)
end_time = time.time()
print(f"\n TRAINING COMPLETE in {(end_time - start_time)/60:.1f} minutes.")
# 5. Visualization
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Test Accuracy')
plt.title('Accuracy Curve (Should go > 80%)')
plt.legend(); plt.grid(True)
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Test Loss')
plt.title('Loss Curve')
plt.legend(); plt.grid(True)
plt.show()
CPU MODE ACTIVE. Training with StandardScaler. 1. Loading Data... Data Loaded. Shape: (10000, 14, 12) 2. Normalizing Features (StandardScaler)... Scaling complete. Radar and Optical are now balanced.
/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
3. Starting Training (Scaled)... Epoch 1/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.4972 - loss: 0.7782 Epoch 1: val_loss improved from inf to 0.69851, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Scaled.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 8s 16ms/step - accuracy: 0.4972 - loss: 0.7780 - val_accuracy: 0.5015 - val_loss: 0.6985 Epoch 2/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.4995 - loss: 0.7286 Epoch 2: val_loss did not improve from 0.69851 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.4996 - loss: 0.7285 - val_accuracy: 0.5085 - val_loss: 0.6991 Epoch 3/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.4967 - loss: 0.7143 Epoch 3: val_loss did not improve from 0.69851 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 15ms/step - accuracy: 0.4968 - loss: 0.7143 - val_accuracy: 0.5040 - val_loss: 0.7036 Epoch 4/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.5163 - loss: 0.7037 Epoch 4: val_loss did not improve from 0.69851 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.5162 - loss: 0.7037 - val_accuracy: 0.4970 - val_loss: 0.7024 Epoch 5/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.5171 - loss: 0.7026 Epoch 5: val_loss did not improve from 0.69851 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.5170 - loss: 0.7027 - val_accuracy: 0.4820 - val_loss: 0.6991 Epoch 6/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.4932 - loss: 0.7063 Epoch 6: val_loss improved from 0.69851 to 0.69743, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Scaled.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 17ms/step - accuracy: 0.4934 - loss: 0.7062 - val_accuracy: 0.5060 - val_loss: 0.6974 Epoch 7/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5051 - loss: 0.6998 Epoch 7: val_loss did not improve from 0.69743 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5052 - loss: 0.6998 - val_accuracy: 0.5050 - val_loss: 0.7005 Epoch 8/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.5001 - loss: 0.7023 Epoch 8: val_loss improved from 0.69743 to 0.69526, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Scaled.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 8s 32ms/step - accuracy: 0.5001 - loss: 0.7023 - val_accuracy: 0.4915 - val_loss: 0.6953 Epoch 9/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5018 - loss: 0.7017 Epoch 9: val_loss did not improve from 0.69526 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 14ms/step - accuracy: 0.5019 - loss: 0.7017 - val_accuracy: 0.5110 - val_loss: 0.6960 Epoch 10/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5181 - loss: 0.6950 Epoch 10: val_loss did not improve from 0.69526 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5181 - loss: 0.6950 - val_accuracy: 0.5035 - val_loss: 0.6991 Epoch 11/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5170 - loss: 0.6959 Epoch 11: val_loss did not improve from 0.69526 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5170 - loss: 0.6959 - val_accuracy: 0.4900 - val_loss: 0.6984 Epoch 12/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5191 - loss: 0.6964 Epoch 12: val_loss did not improve from 0.69526 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5191 - loss: 0.6964 - val_accuracy: 0.4925 - val_loss: 0.7002 Epoch 13/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5249 - loss: 0.6941 Epoch 13: val_loss did not improve from 0.69526 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5248 - loss: 0.6941 - val_accuracy: 0.5065 - val_loss: 0.6990 Epoch 14/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5239 - loss: 0.6938 Epoch 14: val_loss improved from 0.69526 to 0.69519, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Scaled.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.5239 - loss: 0.6938 - val_accuracy: 0.5010 - val_loss: 0.6952 Epoch 15/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5229 - loss: 0.6939 Epoch 15: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5229 - loss: 0.6939 - val_accuracy: 0.4875 - val_loss: 0.6980 Epoch 16/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5241 - loss: 0.6933 Epoch 16: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5240 - loss: 0.6933 - val_accuracy: 0.4965 - val_loss: 0.7006 Epoch 17/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5267 - loss: 0.6930 Epoch 17: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5267 - loss: 0.6930 - val_accuracy: 0.4905 - val_loss: 0.6970 Epoch 18/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5092 - loss: 0.6943 Epoch 18: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 13ms/step - accuracy: 0.5092 - loss: 0.6943 - val_accuracy: 0.4840 - val_loss: 0.6993 Epoch 19/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5270 - loss: 0.6915 Epoch 19: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5269 - loss: 0.6915 - val_accuracy: 0.4990 - val_loss: 0.6978 Epoch 20/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5190 - loss: 0.6922 Epoch 20: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5190 - loss: 0.6922 - val_accuracy: 0.4925 - val_loss: 0.6979 Epoch 21/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5178 - loss: 0.6941 Epoch 21: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5178 - loss: 0.6941 - val_accuracy: 0.4845 - val_loss: 0.6996 Epoch 22/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5202 - loss: 0.6937 Epoch 22: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 17ms/step - accuracy: 0.5202 - loss: 0.6936 - val_accuracy: 0.4900 - val_loss: 0.6980 Epoch 23/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5272 - loss: 0.6903 Epoch 23: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5272 - loss: 0.6903 - val_accuracy: 0.4820 - val_loss: 0.7021 Epoch 24/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5220 - loss: 0.6935 Epoch 24: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 13ms/step - accuracy: 0.5220 - loss: 0.6935 - val_accuracy: 0.4850 - val_loss: 0.6985 Epoch 25/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5177 - loss: 0.6925 Epoch 25: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 18ms/step - accuracy: 0.5178 - loss: 0.6925 - val_accuracy: 0.4860 - val_loss: 0.6982 Epoch 26/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5167 - loss: 0.6937 Epoch 26: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5168 - loss: 0.6937 - val_accuracy: 0.4820 - val_loss: 0.6995 Epoch 27/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5192 - loss: 0.6930 Epoch 27: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5191 - loss: 0.6931 - val_accuracy: 0.4725 - val_loss: 0.6977 Epoch 28/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.5310 - loss: 0.6897 Epoch 28: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5310 - loss: 0.6897 - val_accuracy: 0.5065 - val_loss: 0.6965 Epoch 29/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5286 - loss: 0.6916 Epoch 29: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5285 - loss: 0.6916 - val_accuracy: 0.4850 - val_loss: 0.6995 Epoch 30/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5190 - loss: 0.6926 Epoch 30: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5191 - loss: 0.6926 - val_accuracy: 0.4760 - val_loss: 0.6991 Epoch 31/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5256 - loss: 0.6907 Epoch 31: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.5257 - loss: 0.6907 - val_accuracy: 0.4875 - val_loss: 0.6990 Epoch 32/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5297 - loss: 0.6909 Epoch 32: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5297 - loss: 0.6909 - val_accuracy: 0.4905 - val_loss: 0.7030 Epoch 33/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5430 - loss: 0.6878 Epoch 33: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5429 - loss: 0.6879 - val_accuracy: 0.5110 - val_loss: 0.6979 Epoch 34/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5359 - loss: 0.6887 Epoch 34: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 7s 20ms/step - accuracy: 0.5359 - loss: 0.6887 - val_accuracy: 0.5020 - val_loss: 0.7013 Epoch 35/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5419 - loss: 0.6891 Epoch 35: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5419 - loss: 0.6891 - val_accuracy: 0.4920 - val_loss: 0.7009 Epoch 36/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5274 - loss: 0.6889 Epoch 36: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5274 - loss: 0.6889 - val_accuracy: 0.4975 - val_loss: 0.7003 Epoch 37/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.5220 - loss: 0.6903 Epoch 37: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.5220 - loss: 0.6903 - val_accuracy: 0.4890 - val_loss: 0.6987 Epoch 38/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.5277 - loss: 0.6910 Epoch 38: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 15ms/step - accuracy: 0.5277 - loss: 0.6910 - val_accuracy: 0.5010 - val_loss: 0.6993 Epoch 39/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5555 - loss: 0.6839 Epoch 39: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5552 - loss: 0.6840 - val_accuracy: 0.4960 - val_loss: 0.7010 Epoch 40/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5402 - loss: 0.6869 Epoch 40: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5402 - loss: 0.6869 - val_accuracy: 0.4900 - val_loss: 0.7009 Epoch 41/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.5483 - loss: 0.6851 Epoch 41: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5482 - loss: 0.6851 - val_accuracy: 0.4785 - val_loss: 0.7025 Epoch 42/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5478 - loss: 0.6840 Epoch 42: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5478 - loss: 0.6840 - val_accuracy: 0.5010 - val_loss: 0.7015 Epoch 43/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5385 - loss: 0.6860 Epoch 43: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5385 - loss: 0.6860 - val_accuracy: 0.4785 - val_loss: 0.7054 Epoch 44/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.5508 - loss: 0.6839 Epoch 44: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 17ms/step - accuracy: 0.5507 - loss: 0.6840 - val_accuracy: 0.4975 - val_loss: 0.7024 Epoch 45/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.5471 - loss: 0.6870 Epoch 45: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.5471 - loss: 0.6870 - val_accuracy: 0.5025 - val_loss: 0.7030 Epoch 46/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5458 - loss: 0.6853 Epoch 46: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5458 - loss: 0.6853 - val_accuracy: 0.4865 - val_loss: 0.7055 Epoch 47/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5553 - loss: 0.6822 Epoch 47: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - accuracy: 0.5553 - loss: 0.6822 - val_accuracy: 0.5035 - val_loss: 0.7048 Epoch 48/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5548 - loss: 0.6829 Epoch 48: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 18ms/step - accuracy: 0.5548 - loss: 0.6829 - val_accuracy: 0.4995 - val_loss: 0.7032 Epoch 49/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5543 - loss: 0.6814 Epoch 49: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5543 - loss: 0.6815 - val_accuracy: 0.5105 - val_loss: 0.7031 Epoch 50/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5559 - loss: 0.6802 Epoch 50: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.5558 - loss: 0.6803 - val_accuracy: 0.5125 - val_loss: 0.7021 Epoch 51/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.5651 - loss: 0.6795 Epoch 51: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 19ms/step - accuracy: 0.5650 - loss: 0.6795 - val_accuracy: 0.5080 - val_loss: 0.7020 Epoch 52/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5716 - loss: 0.6766 Epoch 52: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 14ms/step - accuracy: 0.5714 - loss: 0.6767 - val_accuracy: 0.5080 - val_loss: 0.7032 Epoch 53/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5501 - loss: 0.6826 Epoch 53: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 14ms/step - accuracy: 0.5502 - loss: 0.6826 - val_accuracy: 0.4925 - val_loss: 0.7053 Epoch 54/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5464 - loss: 0.6800 Epoch 54: val_loss did not improve from 0.69519 250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 18ms/step - accuracy: 0.5464 - loss: 0.6800 - val_accuracy: 0.5015 - val_loss: 0.7059 Epoch 54: early stopping Restoring model weights from the end of the best epoch: 14. TRAINING COMPLETE in 4.1 minutes.
In [26]:
# PART 3: THE INTELLIGENCE GATE (LIGHTWEIGHT MODEL)
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import time
import os
# 1. Force CPU Mode
tf.config.set_visible_devices([], 'GPU')
print(" CPU MODE ACTIVE. Training Lightweight Model.")
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
# 2. Load & Scale Data (Re-running just to be safe)
try:
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
# Scale (Critical Step)
N, T, F = X.shape
X_flat = X.reshape(N * T, F)
scaler = StandardScaler()
X_scaled_flat = scaler.fit_transform(X_flat)
X_scaled = X_scaled_flat.reshape(N, T, F)
# Split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
print(" Data Loaded & Scaled.")
except:
raise Exception(" Data missing.")
# 3. The "Lightweight" Architecture
# Single Layer LSTM + High Dropout = Forced Generalization
model = Sequential([
# Reduced from 64 to 32 units
LSTM(32, return_sequences=False, input_shape=(14, 12)),
# Batch Normalization keeps weights stable
BatchNormalization(),
# Increased Dropout (50% of neurons turned off randomly)
# This forces the model to not rely on any single feature
Dropout(0.5),
# Simple Decision Layer
Dense(16, activation='relu'),
Dropout(0.2), # Extra dropout before final decision
Dense(1, activation='sigmoid')
])
model.compile(optimizer=Adam(learning_rate=0.0005), # Gentle learning rate
loss='binary_crossentropy',
metrics=['accuracy'])
callbacks = [
# Patience 40 is enough for a small model
EarlyStopping(monitor='val_loss', patience=40, verbose=1, restore_best_weights=True),
ModelCheckpoint(INPUT_DIR + 'Best_Wheat_LSTM_Light.h5', monitor='val_loss', save_best_only=True, verbose=1)
]
print("\n3. Starting Training (Lightweight)...")
start_time = time.time()
history = model.fit(
X_train, y_train,
epochs=200,
batch_size=32,
validation_data=(X_test, y_test),
callbacks=callbacks,
verbose=1
)
end_time = time.time()
print(f"\n TRAINING COMPLETE in {(end_time - start_time)/60:.1f} minutes.")
# Visualization
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train')
plt.plot(history.history['val_accuracy'], label='Test')
plt.title('Accuracy (Lightweight)')
plt.legend(); plt.grid(True)
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train')
plt.plot(history.history['val_loss'], label='Test')
plt.title('Loss (Lightweight)')
plt.legend(); plt.grid(True)
plt.show()
CPU MODE ACTIVE. Training Lightweight Model. Data Loaded & Scaled.
/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
3. Starting Training (Lightweight)... Epoch 1/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5023 - loss: 0.8261 Epoch 1: val_loss improved from inf to 0.69800, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 7s 8ms/step - accuracy: 0.5024 - loss: 0.8249 - val_accuracy: 0.4895 - val_loss: 0.6980 Epoch 2/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5126 - loss: 0.7570 Epoch 2: val_loss did not improve from 0.69800 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5125 - loss: 0.7569 - val_accuracy: 0.5035 - val_loss: 0.6986 Epoch 3/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.4978 - loss: 0.7320 Epoch 3: val_loss did not improve from 0.69800 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.4978 - loss: 0.7319 - val_accuracy: 0.5010 - val_loss: 0.7001 Epoch 4/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5090 - loss: 0.7098 Epoch 4: val_loss improved from 0.69800 to 0.69690, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5089 - loss: 0.7099 - val_accuracy: 0.5110 - val_loss: 0.6969 Epoch 5/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5104 - loss: 0.7076 Epoch 5: val_loss improved from 0.69690 to 0.69356, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5101 - loss: 0.7077 - val_accuracy: 0.5125 - val_loss: 0.6936 Epoch 6/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5013 - loss: 0.7043 Epoch 6: val_loss improved from 0.69356 to 0.69346, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.5014 - loss: 0.7043 - val_accuracy: 0.5120 - val_loss: 0.6935 Epoch 7/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5025 - loss: 0.7065 Epoch 7: val_loss did not improve from 0.69346 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.5024 - loss: 0.7065 - val_accuracy: 0.5075 - val_loss: 0.6935 Epoch 8/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5044 - loss: 0.7022 Epoch 8: val_loss did not improve from 0.69346 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5043 - loss: 0.7022 - val_accuracy: 0.4955 - val_loss: 0.6938 Epoch 9/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5046 - loss: 0.7000 Epoch 9: val_loss did not improve from 0.69346 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5046 - loss: 0.7000 - val_accuracy: 0.4955 - val_loss: 0.6942 Epoch 10/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5101 - loss: 0.6962 Epoch 10: val_loss did not improve from 0.69346 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5099 - loss: 0.6962 - val_accuracy: 0.5015 - val_loss: 0.6936 Epoch 11/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.4968 - loss: 0.6994 Epoch 11: val_loss did not improve from 0.69346 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.4970 - loss: 0.6993 - val_accuracy: 0.5045 - val_loss: 0.6936 Epoch 12/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5041 - loss: 0.6974 Epoch 12: val_loss improved from 0.69346 to 0.69345, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.5041 - loss: 0.6974 - val_accuracy: 0.5025 - val_loss: 0.6934 Epoch 13/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5004 - loss: 0.6951 Epoch 13: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5005 - loss: 0.6952 - val_accuracy: 0.4940 - val_loss: 0.6937 Epoch 14/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.4984 - loss: 0.6953 Epoch 14: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.4984 - loss: 0.6954 - val_accuracy: 0.4955 - val_loss: 0.6935 Epoch 15/200 243/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5057 - loss: 0.6948 Epoch 15: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5057 - loss: 0.6948 - val_accuracy: 0.4950 - val_loss: 0.6937 Epoch 16/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5074 - loss: 0.6939 Epoch 16: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5075 - loss: 0.6939 - val_accuracy: 0.4925 - val_loss: 0.6940 Epoch 17/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5100 - loss: 0.6937 Epoch 17: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5099 - loss: 0.6937 - val_accuracy: 0.4825 - val_loss: 0.6940 Epoch 18/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5052 - loss: 0.6939 Epoch 18: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5053 - loss: 0.6939 - val_accuracy: 0.4825 - val_loss: 0.6940 Epoch 19/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5040 - loss: 0.6948 Epoch 19: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5040 - loss: 0.6948 - val_accuracy: 0.4950 - val_loss: 0.6941 Epoch 20/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5080 - loss: 0.6942 Epoch 20: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5080 - loss: 0.6942 - val_accuracy: 0.4915 - val_loss: 0.6939 Epoch 21/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.5172 - loss: 0.6926 Epoch 21: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.5172 - loss: 0.6926 - val_accuracy: 0.4890 - val_loss: 0.6942 Epoch 22/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5121 - loss: 0.6941 Epoch 22: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5122 - loss: 0.6941 - val_accuracy: 0.4850 - val_loss: 0.6941 Epoch 23/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5071 - loss: 0.6928 Epoch 23: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5073 - loss: 0.6928 - val_accuracy: 0.5005 - val_loss: 0.6935 Epoch 24/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5042 - loss: 0.6929 Epoch 24: val_loss did not improve from 0.69345 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5043 - loss: 0.6929 - val_accuracy: 0.4935 - val_loss: 0.6935 Epoch 25/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5100 - loss: 0.6929 Epoch 25: val_loss improved from 0.69345 to 0.69343, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5101 - loss: 0.6929 - val_accuracy: 0.4950 - val_loss: 0.6934 Epoch 26/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5175 - loss: 0.6925 Epoch 26: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5174 - loss: 0.6925 - val_accuracy: 0.4990 - val_loss: 0.6935 Epoch 27/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5189 - loss: 0.6921 Epoch 27: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.5187 - loss: 0.6921 - val_accuracy: 0.4885 - val_loss: 0.6936 Epoch 28/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.5064 - loss: 0.6926 Epoch 28: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.5065 - loss: 0.6926 - val_accuracy: 0.4925 - val_loss: 0.6940 Epoch 29/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5100 - loss: 0.6937 Epoch 29: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5099 - loss: 0.6937 - val_accuracy: 0.4905 - val_loss: 0.6938 Epoch 30/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5115 - loss: 0.6921 Epoch 30: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5115 - loss: 0.6921 - val_accuracy: 0.5095 - val_loss: 0.6939 Epoch 31/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5050 - loss: 0.6927 Epoch 31: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.5050 - loss: 0.6927 - val_accuracy: 0.4955 - val_loss: 0.6945 Epoch 32/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5124 - loss: 0.6931 Epoch 32: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5125 - loss: 0.6931 - val_accuracy: 0.5035 - val_loss: 0.6937 Epoch 33/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5042 - loss: 0.6921 Epoch 33: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5044 - loss: 0.6921 - val_accuracy: 0.5115 - val_loss: 0.6936 Epoch 34/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5149 - loss: 0.6924 Epoch 34: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.5150 - loss: 0.6924 - val_accuracy: 0.5055 - val_loss: 0.6942 Epoch 35/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.5288 - loss: 0.6906 Epoch 35: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 9ms/step - accuracy: 0.5287 - loss: 0.6906 - val_accuracy: 0.5085 - val_loss: 0.6940 Epoch 36/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5129 - loss: 0.6919 Epoch 36: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5130 - loss: 0.6919 - val_accuracy: 0.4860 - val_loss: 0.6956 Epoch 37/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5188 - loss: 0.6911 Epoch 37: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5189 - loss: 0.6911 - val_accuracy: 0.5050 - val_loss: 0.6937 Epoch 38/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5223 - loss: 0.6901 Epoch 38: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5223 - loss: 0.6901 - val_accuracy: 0.5115 - val_loss: 0.6941 Epoch 39/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5041 - loss: 0.6930 Epoch 39: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5043 - loss: 0.6930 - val_accuracy: 0.5165 - val_loss: 0.6942 Epoch 40/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5257 - loss: 0.6921 Epoch 40: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5256 - loss: 0.6921 - val_accuracy: 0.5070 - val_loss: 0.6945 Epoch 41/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5291 - loss: 0.6904 Epoch 41: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5289 - loss: 0.6904 - val_accuracy: 0.5085 - val_loss: 0.6946 Epoch 42/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5320 - loss: 0.6904 Epoch 42: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5318 - loss: 0.6904 - val_accuracy: 0.5080 - val_loss: 0.6939 Epoch 43/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.5228 - loss: 0.6920 Epoch 43: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.5227 - loss: 0.6920 - val_accuracy: 0.5075 - val_loss: 0.6947 Epoch 44/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5188 - loss: 0.6918 Epoch 44: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5189 - loss: 0.6918 - val_accuracy: 0.5105 - val_loss: 0.6961 Epoch 45/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5223 - loss: 0.6900 Epoch 45: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5223 - loss: 0.6900 - val_accuracy: 0.5010 - val_loss: 0.6958 Epoch 46/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5251 - loss: 0.6898 Epoch 46: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5251 - loss: 0.6898 - val_accuracy: 0.5055 - val_loss: 0.6957 Epoch 47/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5213 - loss: 0.6894 Epoch 47: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.5214 - loss: 0.6894 - val_accuracy: 0.4970 - val_loss: 0.6962 Epoch 48/200 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5331 - loss: 0.6900 Epoch 48: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.5331 - loss: 0.6900 - val_accuracy: 0.5000 - val_loss: 0.6961 Epoch 49/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5250 - loss: 0.6925 Epoch 49: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5250 - loss: 0.6925 - val_accuracy: 0.5010 - val_loss: 0.6963 Epoch 50/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5339 - loss: 0.6885 Epoch 50: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5335 - loss: 0.6886 - val_accuracy: 0.5090 - val_loss: 0.6965 Epoch 51/200 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5398 - loss: 0.6891 Epoch 51: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5395 - loss: 0.6891 - val_accuracy: 0.5080 - val_loss: 0.6978 Epoch 52/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5366 - loss: 0.6891 Epoch 52: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5361 - loss: 0.6891 - val_accuracy: 0.5015 - val_loss: 0.6972 Epoch 53/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5257 - loss: 0.6897 Epoch 53: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5257 - loss: 0.6897 - val_accuracy: 0.5050 - val_loss: 0.6959 Epoch 54/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5313 - loss: 0.6891 Epoch 54: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.5312 - loss: 0.6890 - val_accuracy: 0.5020 - val_loss: 0.6976 Epoch 55/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5365 - loss: 0.6903 Epoch 55: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5365 - loss: 0.6903 - val_accuracy: 0.5015 - val_loss: 0.6974 Epoch 56/200 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5247 - loss: 0.6883 Epoch 56: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5248 - loss: 0.6883 - val_accuracy: 0.5070 - val_loss: 0.7016 Epoch 57/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5195 - loss: 0.6912 Epoch 57: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5196 - loss: 0.6912 - val_accuracy: 0.5005 - val_loss: 0.6966 Epoch 58/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5397 - loss: 0.6887 Epoch 58: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5395 - loss: 0.6887 - val_accuracy: 0.5125 - val_loss: 0.6967 Epoch 59/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5346 - loss: 0.6884 Epoch 59: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.5344 - loss: 0.6884 - val_accuracy: 0.5070 - val_loss: 0.6976 Epoch 60/200 241/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5378 - loss: 0.6886 Epoch 60: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5376 - loss: 0.6886 - val_accuracy: 0.5005 - val_loss: 0.6978 Epoch 61/200 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5395 - loss: 0.6878 Epoch 61: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5393 - loss: 0.6878 - val_accuracy: 0.4980 - val_loss: 0.6978 Epoch 62/200 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5273 - loss: 0.6891 Epoch 62: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5273 - loss: 0.6891 - val_accuracy: 0.4955 - val_loss: 0.6983 Epoch 63/200 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5384 - loss: 0.6852 Epoch 63: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5383 - loss: 0.6853 - val_accuracy: 0.5010 - val_loss: 0.6992 Epoch 64/200 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5327 - loss: 0.6854 Epoch 64: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5328 - loss: 0.6854 - val_accuracy: 0.4910 - val_loss: 0.6986 Epoch 65/200 242/250 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5399 - loss: 0.6847 Epoch 65: val_loss did not improve from 0.69343 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.5398 - loss: 0.6848 - val_accuracy: 0.5065 - val_loss: 0.6982 Epoch 65: early stopping Restoring model weights from the end of the best epoch: 25. TRAINING COMPLETE in 2.2 minutes.
In [27]:
# ==========================================
# PART 3: THE DIAGNOSTIC (OPTICAL ONLY)
# ==========================================
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import os
# Force CPU
tf.config.set_visible_devices([], 'GPU')
print(" DIAGNOSTIC MODE: Optical Data Only.")
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
try:
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
# --- CRITICAL CHANGE: DROP RADAR ---
# Original Shape: (10000, 14, 12) -> [VV, VH, B2, B3...]
# We want indices 2 through 11 (The 10 Optical Bands)
X_optical = X[:, :, 2:]
print(f" Original Shape: {X.shape}")
print(f" Optical Shape: {X_optical.shape} (Radar Removed)")
# Scale Optical Data
N, T, F = X_optical.shape
X_flat = X_optical.reshape(N * T, F)
scaler = StandardScaler()
X_scaled_flat = scaler.fit_transform(X_flat)
X_scaled = X_scaled_flat.reshape(N, T, F)
# Split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
except:
raise Exception(" Data missing.")
# Model (Standard Architecture)
model = Sequential([
LSTM(64, return_sequences=False, input_shape=(14, 10)), # Input shape is now 10 features
BatchNormalization(),
Dropout(0.4),
Dense(32, activation='relu'),
Dropout(0.2),
Dense(1, activation='sigmoid')
])
model.compile(optimizer=Adam(learning_rate=0.0005),
loss='binary_crossentropy',
metrics=['accuracy'])
callbacks = [
EarlyStopping(monitor='val_loss', patience=30, verbose=1, restore_best_weights=True),
ModelCheckpoint(INPUT_DIR + 'Best_Wheat_LSTM_Optical.h5', monitor='val_loss', save_best_only=True, verbose=1)
]
print("\nStarting Optical-Only Training...")
history = model.fit(
X_train, y_train,
epochs=150,
batch_size=32,
validation_data=(X_test, y_test),
callbacks=callbacks,
verbose=1
)
# Plot
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train')
plt.plot(history.history['val_accuracy'], label='Test')
plt.title('Accuracy (Optical Only)')
plt.legend(); plt.grid(True)
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train')
plt.plot(history.history['val_loss'], label='Test')
plt.title('Loss (Optical Only)')
plt.legend(); plt.grid(True)
plt.show()
DIAGNOSTIC MODE: Optical Data Only. Original Shape: (10000, 14, 12) Optical Shape: (10000, 14, 10) (Radar Removed) Starting Optical-Only Training... Epoch 1/150
/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5009 - loss: 0.8491 Epoch 1: val_loss improved from inf to 0.69641, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 5s 9ms/step - accuracy: 0.5009 - loss: 0.8481 - val_accuracy: 0.4960 - val_loss: 0.6964 Epoch 2/150 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.4998 - loss: 0.7548 Epoch 2: val_loss did not improve from 0.69641 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.4998 - loss: 0.7548 - val_accuracy: 0.4950 - val_loss: 0.6997 Epoch 3/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.4817 - loss: 0.7417 Epoch 3: val_loss did not improve from 0.69641 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 14ms/step - accuracy: 0.4819 - loss: 0.7415 - val_accuracy: 0.4960 - val_loss: 0.7078 Epoch 4/150 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5018 - loss: 0.7216 Epoch 4: val_loss did not improve from 0.69641 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5017 - loss: 0.7216 - val_accuracy: 0.4935 - val_loss: 0.6975 Epoch 5/150 243/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5121 - loss: 0.7129 Epoch 5: val_loss did not improve from 0.69641 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5119 - loss: 0.7129 - val_accuracy: 0.4785 - val_loss: 0.6977 Epoch 6/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5029 - loss: 0.7130 Epoch 6: val_loss improved from 0.69641 to 0.69614, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5028 - loss: 0.7130 - val_accuracy: 0.4920 - val_loss: 0.6961 Epoch 7/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5196 - loss: 0.7021 Epoch 7: val_loss did not improve from 0.69614 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5195 - loss: 0.7021 - val_accuracy: 0.4845 - val_loss: 0.6981 Epoch 8/150 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.5086 - loss: 0.7038 Epoch 8: val_loss did not improve from 0.69614 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5086 - loss: 0.7037 - val_accuracy: 0.5050 - val_loss: 0.6983 Epoch 9/150 243/250 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5078 - loss: 0.7011 Epoch 9: val_loss improved from 0.69614 to 0.69530, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5078 - loss: 0.7011 - val_accuracy: 0.4865 - val_loss: 0.6953 Epoch 10/150 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5040 - loss: 0.6995 Epoch 10: val_loss improved from 0.69530 to 0.69514, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5040 - loss: 0.6995 - val_accuracy: 0.5020 - val_loss: 0.6951 Epoch 11/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5020 - loss: 0.7004 Epoch 11: val_loss did not improve from 0.69514 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5019 - loss: 0.7004 - val_accuracy: 0.4900 - val_loss: 0.6952 Epoch 12/150 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5029 - loss: 0.6972 Epoch 12: val_loss improved from 0.69514 to 0.69420, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5028 - loss: 0.6972 - val_accuracy: 0.4995 - val_loss: 0.6942 Epoch 13/150 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5073 - loss: 0.6976 Epoch 13: val_loss improved from 0.69420 to 0.69319, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5072 - loss: 0.6976 - val_accuracy: 0.5090 - val_loss: 0.6932 Epoch 14/150 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5002 - loss: 0.6974 Epoch 14: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5002 - loss: 0.6974 - val_accuracy: 0.4855 - val_loss: 0.6943 Epoch 15/150 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5099 - loss: 0.6948 Epoch 15: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5099 - loss: 0.6948 - val_accuracy: 0.4965 - val_loss: 0.6932 Epoch 16/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5140 - loss: 0.6951 Epoch 16: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.5139 - loss: 0.6951 - val_accuracy: 0.5045 - val_loss: 0.6962 Epoch 17/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.4858 - loss: 0.7001 Epoch 17: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.4860 - loss: 0.7000 - val_accuracy: 0.5105 - val_loss: 0.6943 Epoch 18/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5167 - loss: 0.6942 Epoch 18: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5164 - loss: 0.6943 - val_accuracy: 0.4895 - val_loss: 0.6934 Epoch 19/150 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5095 - loss: 0.6930 Epoch 19: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.5094 - loss: 0.6930 - val_accuracy: 0.4975 - val_loss: 0.6937 Epoch 20/150 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5106 - loss: 0.6936 Epoch 20: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5104 - loss: 0.6936 - val_accuracy: 0.4950 - val_loss: 0.6945 Epoch 21/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5172 - loss: 0.6926 Epoch 21: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5172 - loss: 0.6926 - val_accuracy: 0.4840 - val_loss: 0.6947 Epoch 22/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5125 - loss: 0.6933 Epoch 22: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5125 - loss: 0.6933 - val_accuracy: 0.4895 - val_loss: 0.6952 Epoch 23/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5068 - loss: 0.6934 Epoch 23: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5069 - loss: 0.6934 - val_accuracy: 0.5030 - val_loss: 0.6936 Epoch 24/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5050 - loss: 0.6940 Epoch 24: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.5051 - loss: 0.6940 - val_accuracy: 0.4925 - val_loss: 0.6942 Epoch 25/150 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5161 - loss: 0.6917 Epoch 25: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5161 - loss: 0.6917 - val_accuracy: 0.4885 - val_loss: 0.6948 Epoch 26/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5190 - loss: 0.6928 Epoch 26: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 10ms/step - accuracy: 0.5189 - loss: 0.6928 - val_accuracy: 0.4890 - val_loss: 0.6935 Epoch 27/150 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5096 - loss: 0.6939 Epoch 27: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 8ms/step - accuracy: 0.5097 - loss: 0.6939 - val_accuracy: 0.4830 - val_loss: 0.6939 Epoch 28/150 245/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5039 - loss: 0.6923 Epoch 28: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5040 - loss: 0.6923 - val_accuracy: 0.4870 - val_loss: 0.6941 Epoch 29/150 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5138 - loss: 0.6928 Epoch 29: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5138 - loss: 0.6928 - val_accuracy: 0.5040 - val_loss: 0.6932 Epoch 30/150 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5336 - loss: 0.6902 Epoch 30: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.5331 - loss: 0.6902 - val_accuracy: 0.4760 - val_loss: 0.6941 Epoch 31/150 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.5186 - loss: 0.6929 Epoch 31: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5187 - loss: 0.6929 - val_accuracy: 0.4920 - val_loss: 0.6934 Epoch 32/150 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5190 - loss: 0.6930 Epoch 32: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 10ms/step - accuracy: 0.5190 - loss: 0.6930 - val_accuracy: 0.5045 - val_loss: 0.6935 Epoch 33/150 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5188 - loss: 0.6910 Epoch 33: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5185 - loss: 0.6911 - val_accuracy: 0.5045 - val_loss: 0.6932 Epoch 34/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5090 - loss: 0.6932 Epoch 34: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5091 - loss: 0.6932 - val_accuracy: 0.4930 - val_loss: 0.6947 Epoch 35/150 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5163 - loss: 0.6917 Epoch 35: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5164 - loss: 0.6917 - val_accuracy: 0.4990 - val_loss: 0.6934 Epoch 36/150 244/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5213 - loss: 0.6913 Epoch 36: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5212 - loss: 0.6913 - val_accuracy: 0.4960 - val_loss: 0.6938 Epoch 37/150 247/250 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5143 - loss: 0.6923 Epoch 37: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.5143 - loss: 0.6923 - val_accuracy: 0.5015 - val_loss: 0.6935 Epoch 38/150 243/250 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.5197 - loss: 0.6913 Epoch 38: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.5197 - loss: 0.6913 - val_accuracy: 0.4905 - val_loss: 0.6940 Epoch 39/150 246/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5255 - loss: 0.6899 Epoch 39: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5254 - loss: 0.6899 - val_accuracy: 0.4925 - val_loss: 0.6940 Epoch 40/150 249/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5231 - loss: 0.6915 Epoch 40: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5231 - loss: 0.6915 - val_accuracy: 0.4945 - val_loss: 0.6947 Epoch 41/150 248/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5206 - loss: 0.6914 Epoch 41: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5206 - loss: 0.6914 - val_accuracy: 0.5075 - val_loss: 0.6942 Epoch 42/150 243/250 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5306 - loss: 0.6911 Epoch 42: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.5306 - loss: 0.6911 - val_accuracy: 0.4955 - val_loss: 0.6941 Epoch 43/150 250/250 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5353 - loss: 0.6885 Epoch 43: val_loss did not improve from 0.69319 250/250 ━━━━━━━━━━━━━━━━━━━━ 3s 12ms/step - accuracy: 0.5352 - loss: 0.6885 - val_accuracy: 0.4960 - val_loss: 0.6945 Epoch 43: early stopping Restoring model weights from the end of the best epoch: 13.
In [28]:
# ==========================================
# PART 4: THE ULTIMATE SEPARABILITY TEST
# ==========================================
import numpy as np
import matplotlib.pyplot as plt
INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'
try:
# 1. Load Data
X = np.load(INPUT_DIR + 'X_wheat.npy')
y = np.load(INPUT_DIR + 'y_wheat.npy')
print(f"Data Loaded: {X.shape}")
# 2. Separate Wheat and Non-Wheat
wheat_indices = np.where(y == 1)[0]
non_wheat_indices = np.where(y == 0)[0]
X_wheat = X[wheat_indices]
X_non_wheat = X[non_wheat_indices]
# 3. Calculate the AVERAGE Farm for each class
# We take the mean across all farms (axis 0)
# Shape becomes (14, 12) -> Average value for each date/band
avg_wheat = np.mean(X_wheat, axis=0)
avg_non_wheat = np.mean(X_non_wheat, axis=0)
# 4. Plot the comparison for Key Bands
# Band 2 (Blue - Index 2) and Band 8 (NIR - Index 8) are critical
# Radar VV (Index 0) is critical
plt.figure(figsize=(18, 5))
# --- PLOT 1: OPTICAL (NIR - Band 8) ---
plt.subplot(1, 3, 1)
plt.plot(avg_wheat[:, 8], label='Wheat (Avg)', color='green', linewidth=3)
plt.plot(avg_non_wheat[:, 8], label='Non-Wheat (Avg)', color='orange', linewidth=3, linestyle='--')
plt.title("AVERAGE Optical Growth (NIR - B8)")
plt.xlabel("Time (Fortnights)")
plt.ylabel("Reflectance")
plt.legend()
plt.grid(True)
# --- PLOT 2: RADAR (VV - Index 0) ---
plt.subplot(1, 3, 2)
plt.plot(avg_wheat[:, 0], label='Wheat (Avg)', color='blue', linewidth=3)
plt.plot(avg_non_wheat[:, 0], label='Non-Wheat (Avg)', color='red', linewidth=3, linestyle='--')
plt.title("AVERAGE Radar Signal (VV)")
plt.xlabel("Time (Fortnights)")
plt.ylabel("Backscatter (Linear)")
plt.legend()
plt.grid(True)
# --- PLOT 3: OPTICAL (Red Edge - B5 - Index 5) ---
plt.subplot(1, 3, 3)
plt.plot(avg_wheat[:, 5], label='Wheat (Avg)', color='purple', linewidth=3)
plt.plot(avg_non_wheat[:, 5], label='Non-Wheat (Avg)', color='brown', linewidth=3, linestyle='--')
plt.title("AVERAGE Red Edge (B5)")
plt.xlabel("Time (Fortnights)")
plt.legend()
plt.grid(True)
plt.show()
except Exception as e:
print(f"Error: {e}")
Data Loaded: (10000, 14, 12)
References Wheat crop detection combining NDVI time series and phenology, Haridwar district, India (2025). Journal of Geography and Cartography. Methodology combining temporal NDVI with CNN/RF; RF superior to CNN for phenology (69% vs. 62% accuracy) due to better handling of phenological features.
Fodder crop estimation using Sentinel-2A/B, West Bengal, October-March (2019). Indian Journal of Agricultural Sciences. Multi-date NDVI spectral profiles to differentiate fodder from other crops (81.6% fodder accuracy). Shows berseem discrimination using temporal signatures.
Temporal Sentinel-2 imagery for wheat mapping, Nepal (2025). ISPRS Annals of Photogrammetry. Random Forest with phenological stages; 99% training, 86% validation accuracy. Demonstrates 10 m resolution effectiveness for small farms.
Crop type identification using Sentinel-2 with focus on field-level info, Rajasthan (2020). Taylor & Francis Online. Spectral Matching Technique (SMT) based on temporal NDVI signatures; 84% overall accuracy (86% wheat, 94% mustard). Confirms temporal signatures easily distinguish wheat from competing crops.
Wheat area mapping and phenology detection, Punjab/Haryana (2019). ISPRS Archives. Sentinel-1 and Sentinel-2 combined; 88–91% accuracy for wheat classification. Early season mapping using Oct-Dec sowing detection.
Crop type identification and spatial mapping using Sentinel-2, India (2020). Peer-reviewed study showing temporal signatures of wheat, chickpea, and mustard are easily distinguishable, achieving 84% overall accuracy with 86% wheat and 94% mustard accuracies using phenological matching.
Automated in-season CDL-like products for USA (2024). IEEE. Random Forest on Sentinel-2/Landsat with historical CDL training; achieved F1 scores of 0.911 (corn), 0.959 (soybean). Demonstrates trusted-pixel approach for generating crop maps without ground truth.
[85-88] ESA WorldCover 2020/2021 products. 10 m global land cover classification using Sentinel-1/2 data. UN-LCCS classification system with 11 classes including cropland, built-up, water, trees. Freely available in Google Earth Engine.
Crop classification using Sentinel-1 SAR time-series (2025). Remote Sensing. LSTM, Bi-GRU, TCN with attention mechanisms; TCN+attention achieved 85.7% accuracy. Shows attention mechanisms improve crop classification from temporal features.
Deep learning for multi-temporal crop classification (2023). Agronomy. Compared 1D-CNN (92.5%), LSTM (93.25%), 2D-CNN (94.76%), 3D-CNN, ConvLSTM2D using Sentinel-2. LSTM outperformed Random Forest (~91%) for temporal features; 2D-CNN highest overall.
Deep learning with combined satellite and weather data (2022). ISPRS Archives. LSTM vs. Transformer for crop classification using Sentinel-2 + NWP weather data; FlexMod framework. Shows temporal encoding importance for phenology capture.
Advancing crop classification in smallholder agriculture (2024). PLOS One. Bi-LSTM achieved 96% validation accuracy for Kharif, 94% for Rabi seasons. Bidirectional LSTM more effective than unidirectional for capturing phenological context from both growth and maturity phases.
Crop cover identification using NDVI/BNDVI/GNDVI time-series (2024). Environmental Research and Technology. ARIMA/LSTM/Prophet models for wheat-mustard-sugarcane discrimination, October-April; LSTM minimum RMSE for wheat (0.036), mustard (0.026), sugarcane (0.054) using different indices.
In [ ]:
OLMOEARTH
why bilstm is better than lstm https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0299350
Bi-LSTM: 94–96% accuracy for crop classification (vs. RF 88–92%), particularly effective with abundant labeled data and GPU resources
TWDTW: 88–92% accuracy, exceptionally robust with limited training samples (<50 fields), more interpretable, and computationally efficient
Bi-LSTM: 94–96% accuracy for crop classification (vs. RF 88–92%), particularly effective with abundant labeled data and GPU resources
TWDTW: 88–92% accuracy, exceptionally robust with limited training samples (<50 fields), more interpretable, and computationally efficient
| Study | Dataset | Bi-LSTM Accuracy | RF/SVM Baseline | Improvement |
|---|---|---|---|---|
| Khan et al. (2024)plos+1 | Sentinel-2 + PlanetScope (smallholder farms, Pakistan) | 94–96% | 87–90% (RF, SVM, k-NN) | +4–9 pp |
| Bandar et al. (2024)dergipark | Sentinel-2 BreizhCrop | >93% | 89–92% (Vanilla LSTM, CNN, SVM) | +1–4 pp |
| Sentinel-1 SAR study (2024)mdpi | Sentinel-1 rice detection | Higher | Lower (traditional ML) | Significant |
| Rußwurm et al. (2020, CVPR)openaccess.thecvf | Sentinel-2 temporal sequence | 92–94% | 88–90% (CNN, SVM) | +2–4 pp |
| Early-season Canada (2024)ieeexplore.ieee | RCM + Sentinel-1/2 (June–July) | 85% (early) | 82% (XGBoost) | +3 pp |
Why Bi-LSTM Outperforms LSTM (93–95% vs. 94–96%) Standard LSTM only processes sequences forward (Oct → Dec). It misses crucial information:
Growth phase: "NDVI rising → likely tillering → wheat (not rice)"
Senescence phase: "NDVI falling after peak → mature wheat (not growing maize)"
Bi-LSTM adds the backward view:
Grain filling phase happens after peak NDVI
A pixel with falling NDVI in late Dec could be wheat heading/early grain fill OR senescent rice
Backward LSTM differentiates: "Dec 1 looks mature, Dec 15 is declining → mature phase pattern → wheat"
Empirical gain: 1–3 percentage points (94–96% Bi-LSTM vs. 93–95% LSTM).
| Factor | Bi-LSTM | TWDTW | Random Forest (Mohite) | Best for Wheat |
|---|---|---|---|---|
| Accuracy | 90–94% | 88–92% | 88.31% | Bi-LSTM (if data abundant) |
| Small samples (<50) | 88–91% | 92% | 75% | TWDTW |
| Computation | 800 GPU hrs | 50 CPU min | 10 min | TWDTW |
| Interpretability | Low (black-box) | High (patterns) | Medium | TWDTW |
| Phenology extraction | Hard | Built-in | Manual thresholding | TWDTW |
| Temporal gap handling | Excellent (bidirectional) | Good | Poor (cloud-sensitive) | Bi-LSTM |
| Transferability | Retraining needed | Pattern adjustment | Retraining needed | TWDTW |
| Publication novelty | High (2025) | Medium (2019–2023) | Low (2019) | Bi-LSTM |
| Deployment timeline | Weeks to months | Days | Days | TWDTW |
| Method | Expected Accuracy | Implementation Time | Compute Cost | Interpretability | Best For |
|---|---|---|---|---|---|
| Random Forest (Mohite et al., 2019) | 88.31% | 1 day | 10 min | Medium | Baseline |
| Bi-LSTM (new approach) | 90–94% | 2–4 weeks | 800 GPU hrs | Low (black-box) | High accuracy, research novelty |
| TWDTW (new approach) | 88–92% | 3–5 days | 50 CPU min | High | Rapid deployment, interpretability |
| Bi-LSTM + TWDTW Ensemble | 92–95% | 1 month | 800 GPU + 50 CPU | Medium | Maximum accuracy, robustness |
Definition Pixel-based classification treats each individual pixel (10m × 10m for Sentinel-2) as an independent unit that must be classified by itself, using only its own temporal spectral signature—ignoring what neighboring pixels are classified as.
Definition Object-based classification first segments the image into objects (groups of adjacent similar pixels), then classifies each entire object as a unit using the averaged spectral signature of all pixels within that object.
How Object-based TWDTW Works For wheat mapping using object-based TWDTW:
Step 1: Image Segmentation
Divide image into meaningful objects that match agricultural fields
Objects typically contain 100–160 pixels (representing 1–2.5 hectares)
Segmentation ensures objects are spectrally and spatially homogeneous
Wheat area mapping and phenology detection, Punjab/Haryana (2019). ISPRS Archives. Sentinel-1 and Sentinel-2 combined; 88–91% accuracy for wheat classification. Early season mapping using Oct-Dec sowing detection.
