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SETUP\n", "drive.mount('/content/drive', force_remount=True)\n", "MY_PROJECT_ID = '[REDACTED_FOR_SECURITY]'\n", "\n", "try:\n", " ee.Initialize(project=MY_PROJECT_ID)\n", " print(f\"GEE Initialized: {MY_PROJECT_ID}\")\n", "except:\n", " ee.Authenticate()\n", " ee.Initialize(project=MY_PROJECT_ID)\n", "\n", "# CONFIG\n", "WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'\n", "SAVE_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "if not os.path.exists(SAVE_DIR): os.makedirs(SAVE_DIR)\n", "\n", "START_DATE = '2023-10-01'\n", "END_DATE = '2024-04-30'\n", "TOTAL_POINTS = 10000\n", "SPLIT = TOTAL_POINTS // 2\n", "\n", "# --- 2. BALANCED SAMPLING ---\n", "print(\"Generating 50/50 Balanced Samples...\")\n", "wheat_mask = ee.Image(WHEAT_MASK_ASSET)\n", "bounds = wheat_mask.geometry()\n", "\n", "# A. Wheat (Class 1)\n", "wheat_points = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=42) \\\n", " .filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())) \\\n", " .limit(SPLIT).map(lambda f: f.set('class', 1))\n", "\n", "# B. Non-Wheat (Class 0)\n", "non_wheat_points = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=99) \\\n", " .filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())) \\\n", " .limit(SPLIT).map(lambda f: f.set('class', 0))\n", "\n", "points_for_analysis = wheat_points.merge(non_wheat_points)\n", "print(f\"Total Points: {points_for_analysis.size().getInfo()}\")\n", "\n", "# --- 3. SATELLITE PROCESSING (SAFE LEE FILTER) ---\n", "\n", "def apply_lee_filter_safe(image):\n", " \"\"\"\n", " Safe Lee Filter: Uses a CONSTANT (0.004) for noise variance.\n", " This prevents the 'Dictionary.get' crash on defective images.\n", " \"\"\"\n", " def lee_single(b):\n", " img_band = image.select(b)\n", "\n", " # Local Statistics (3x3 Kernel)\n", " weights = ee.Kernel.square(3)\n", " mean = img_band.reduceNeighborhood(ee.Reducer.mean(), weights)\n", " variance = img_band.reduceNeighborhood(ee.Reducer.variance(), weights)\n", "\n", " # CRITICAL FIX: Use Constant 0.004 (Standard for S1 IW)\n", " overall_var_img = ee.Image.constant(0.004)\n", "\n", " k = variance.divide(variance.add(overall_var_img))\n", " return mean.add(k.multiply(img_band.subtract(mean))).rename(b)\n", "\n", " return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)\n", "\n", "def maskS2clouds(image):\n", " qa = image.select('QA60')\n", " mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))\n", " return image.updateMask(mask).select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']) \\\n", " .copyProperties(image, [\"system:time_start\"])\n", "\n", "# --- 4. ROBUST COLLECTIONS ---\n", "s1_col = ee.ImageCollection('COPERNICUS/S1_GRD') \\\n", " .filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.eq('instrumentMode', 'IW')) \\\n", " .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING')) \\\n", " .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) \\\n", " .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH')) \\\n", " .map(apply_lee_filter_safe).select(['VV', 'VH'])\n", "\n", "s2_col = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \\\n", " .filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)) \\\n", " .map(maskS2clouds)\n", "\n", "# --- 5. DAILY COMPOSITES ---\n", "def make_daily_composite(collection, check_bands):\n", " dates = collection.aggregate_array('system:time_start') \\\n", " .map(lambda t: ee.Date(t).format('YYYY-MM-dd')).distinct()\n", " def create_composite(date_str):\n", " d = ee.Date(date_str)\n", " # Bounds filter optimization\n", " daily = collection.filterDate(d, d.advance(1, 'day')) \\\n", " .filter(ee.Filter.bounds(points_for_analysis)) \\\n", " .median().set('system:time_start', d.millis())\n", " return daily\n", "\n", " col = ee.ImageCollection.fromImages(dates.map(create_composite))\n", " for b in check_bands:\n", " col = col.filter(ee.Filter.listContains('system:band_names', b))\n", " return col\n", "\n", "daily_s1 = make_daily_composite(s1_col, ['VV', 'VH'])\n", "daily_s2 = make_daily_composite(s2_col, ['B2'])\n", "\n", "# --- 6. EXPORT TASKS (Split & Robust) ---\n", "\n", "def extract_robust(collection, band_names, prefix):\n", " def sample_image(img):\n", " samples = img.select(band_names).reduceRegions(\n", " collection=points_for_analysis,\n", " reducer=ee.Reducer.first(),\n", " scale=10,\n", " tileScale=16\n", " )\n", " return samples.map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))\n", " return collection.map(sample_image).flatten()\n", "\n", "# Export S1\n", "print(\"Submitting Sentinel-1 Task...\")\n", "s1_flat = extract_robust(daily_s1, ['VV', 'VH'], 'S1')\n", "s1_flat = s1_flat.select(['.*'], None, False)\n", "task1 = ee.batch.Export.table.toDrive(\n", " collection=s1_flat, description=f'Wheat_S1_{START_DATE}_{END_DATE}',\n", " folder='LSTM_Wheat_Results', fileNamePrefix='Wheat_S1_Raw', fileFormat='CSV'\n", ")\n", "task1.start()\n", "\n", "# Export S2\n", "print(\"Submitting Sentinel-2 Task...\")\n", "s2_flat = extract_robust(daily_s2, ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12'], 'S2')\n", "s2_flat = s2_flat.select(['.*'], None, False)\n", "task2 = ee.batch.Export.table.toDrive(\n", " collection=s2_flat, description=f'Wheat_S2_{START_DATE}_{END_DATE}',\n", " folder='LSTM_Wheat_Results', fileNamePrefix='Wheat_S2_Raw', fileFormat='CSV'\n", ")\n", "task2.start()\n", "\n", "print(\"Tasks submitted. Proceed to Step 2.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 138 }, "id": "NUl76jatzYmS", "executionInfo": { "status": "ok", "timestamp": 1769704588838, "user_tz": -330, "elapsed": 4737, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "5acc357d-e461-47c4-ccdd-1a01b4e9699d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n", "GEE Initialized: local-dialect-484618-b9\n", "Generating 50/50 Balanced Samples...\n", "Total Points: 10000\n", "Submitting Sentinel-1 Task...\n", "Submitting Sentinel-2 Task...\n", "Tasks submitted. Proceed to Step 2.\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "# PART 1: GEE EXTRACTION\n", "\n", "import ee\n", "import geemap\n", "import os\n", "from google.colab import drive\n", "\n", "# 1. SETUP\n", "drive.mount('/content/drive', force_remount=True)\n", "\n", "try:\n", " ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n", "except:\n", " ee.Authenticate()\n", " ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n", "\n", "# CANCEL STUCK TASKS\n", "print(\"--- CANCELLING STUCK TASKS ---\")\n", "tasks = ee.batch.Task.list()\n", "for t in tasks:\n", " if t.state == 'READY' and 'Wheat' in t.config['description']:\n", " print(f\"Cancelling: {t.config['description']}\")\n", " ee.data.cancelTask(t.id)\n", "\n", "# CONFIG\n", "WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'\n", "START_DATE = '2023-10-01'\n", "END_DATE = '2024-04-30'\n", "\n", "\n", "BATCHES = [\n", " {'name': 'Batch_A', 'seed': 42}, # First 5,000 points\n", " {'name': 'Batch_B', 'seed': 123} # Second 5,000 points\n", "]\n", "POINTS_PER_BATCH = 5000\n", "SPLIT = POINTS_PER_BATCH // 2\n", "\n", "# --- FUNCTIONS ---\n", "def apply_lee_filter_safe(image):\n", " def lee_single(b):\n", " img_band = image.select(b)\n", " weights = ee.Kernel.square(3)\n", " mean = img_band.reduceNeighborhood(ee.Reducer.mean(), weights)\n", " variance = img_band.reduceNeighborhood(ee.Reducer.variance(), weights)\n", " overall_var_img = ee.Image.constant(0.004)\n", " k = variance.divide(variance.add(overall_var_img))\n", " return mean.add(k.multiply(img_band.subtract(mean))).rename(b)\n", " return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)\n", "\n", "def maskS2clouds(image):\n", " qa = image.select('QA60')\n", " mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))\n", " return image.updateMask(mask).select(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']) \\\n", " .copyProperties(image, [\"system:time_start\"])\n", "\n", "def make_daily_composite(collection, points, check_bands):\n", " dates = collection.aggregate_array('system:time_start') \\\n", " .map(lambda t: ee.Date(t).format('YYYY-MM-dd')).distinct()\n", " def create_composite(date_str):\n", " d = ee.Date(date_str)\n", " daily = collection.filterDate(d, d.advance(1, 'day')) \\\n", " .filter(ee.Filter.bounds(points)) \\\n", " .median().set('system:time_start', d.millis())\n", " return daily\n", " col = ee.ImageCollection.fromImages(dates.map(create_composite))\n", " for b in check_bands: col = col.filter(ee.Filter.listContains('system:band_names', b))\n", " return col\n", "\n", "def extract_robust(collection, points, band_names, prefix):\n", " def sample_image(img):\n", " samples = img.select(band_names).reduceRegions(\n", " collection=points,\n", " reducer=ee.Reducer.first(),\n", " scale=10,\n", " tileScale=16\n", " )\n", " return samples.map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))\n", " return collection.map(sample_image).flatten()\n", "\n", "# --- EXECUTE BATCHES ---\n", "wheat_mask = ee.Image(WHEAT_MASK_ASSET)\n", "bounds = wheat_mask.geometry()\n", "\n", "for batch in BATCHES:\n", " batch_name = batch['name']\n", " seed = batch['seed']\n", " print(f\"\\n PREPARING {batch_name} (5,000 Points)...\")\n", "\n", " # 1. Generate Points\n", " wheat_pts = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=seed) \\\n", " .filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())) \\\n", " .limit(SPLIT).map(lambda f: f.set('class', 1))\n", "\n", " non_wheat_pts = ee.FeatureCollection.randomPoints(region=bounds, points=SPLIT*2, seed=seed+99) \\\n", " .filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())) \\\n", " .limit(SPLIT).map(lambda f: f.set('class', 0))\n", "\n", " points = wheat_pts.merge(non_wheat_pts)\n", "\n", " # 2. Prepare Collections\n", " s1_col = ee.ImageCollection('COPERNICUS/S1_GRD') \\\n", " .filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.eq('instrumentMode', 'IW')) \\\n", " .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING')) \\\n", " .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) \\\n", " .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH')) \\\n", " .map(apply_lee_filter_safe).select(['VV', 'VH'])\n", "\n", " s2_col = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \\\n", " .filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)) \\\n", " .map(maskS2clouds)\n", "\n", " daily_s1 = make_daily_composite(s1_col, points, ['VV', 'VH'])\n", " daily_s2 = make_daily_composite(s2_col, points, ['B2'])\n", "\n", " # 3. Submit Tasks\n", " # S1 Export\n", " s1_flat = extract_robust(daily_s1, points, ['VV', 'VH'], 'S1')\n", " s1_flat = s1_flat.select(['.*'], None, False)\n", " task1 = ee.batch.Export.table.toDrive(\n", " collection=s1_flat, description=f'Wheat_S1_{batch_name}',\n", " folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S1_{batch_name}', fileFormat='CSV'\n", " )\n", " task1.start()\n", "\n", " # S2 Export\n", " s2_flat = extract_robust(daily_s2, points, ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12'], 'S2')\n", " s2_flat = s2_flat.select(['.*'], None, False)\n", " task2 = ee.batch.Export.table.toDrive(\n", " collection=s2_flat, description=f'Wheat_S2_{batch_name}',\n", " folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S2_{batch_name}', fileFormat='CSV'\n", " )\n", " task2.start()\n", " print(f\" Tasks for {batch_name} submitted.\")\n", "\n", "print(\"\\n All 4 tasks submitted (2 batches x 2 sensors).\")\n", "print(\"These smaller tasks should start MUCH faster.\")\n", "print(\"Restart the Watchdog script to monitor them.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 329 }, "id": "ksCXFQeJxutN", "executionInfo": { "status": "ok", "timestamp": 1769707346828, "user_tz": -330, "elapsed": 9906, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "497e5490-164a-4398-8071-19ba17066498" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n", "--- CANCELLING STUCK TASKS ---\n", "Cancelling: Wheat_S2_2023-10-01_2024-04-30\n", "Cancelling: Wheat_S1_2023-10-01_2024-04-30\n", "Cancelling: Wheat_S2_2023-10-01_2024-04-30\n", "Cancelling: Wheat_S1_2023-10-01_2024-04-30\n", "Cancelling: Wheat_S2_2023-10-01_2024-04-30\n", "Cancelling: Wheat_S1_2023-10-01_2024-04-30\n", "\n", "🚀 PREPARING Batch_A (5,000 Points)...\n", " Tasks for Batch_A submitted.\n", "\n", "🚀 PREPARING Batch_B (5,000 Points)...\n", " Tasks for Batch_B submitted.\n", "\n", "✅ All 4 tasks submitted (2 batches x 2 sensors).\n", "These smaller tasks should start MUCH faster.\n", "Restart the Watchdog script to monitor them.\n" ] } ] }, { "cell_type": "code", "source": [ "import time\n", "import os\n", "import ee\n", "\n", "FOLDER = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "print(\"--- DEEP SCAN TASK MONITOR (FIXED) ---\")\n", "\n", "while True:\n", " try:\n", " tasks = ee.batch.Task.list()\n", " except Exception as e:\n", " print(f\"Connection glitch, retrying... {e}\")\n", " time.sleep(10)\n", " continue\n", "\n", " # Filter for our specific tasks\n", " my_tasks = [t for t in tasks[:15] if 'Wheat' in t.config['description']]\n", "\n", " print(f\"\\nTime: {time.strftime('%H:%M:%S')}\")\n", " print(f\"{'TASK NAME':<25} | {'STATUS':<12} | {'DRIVE FILE'}\")\n", " print(\"-\" * 60)\n", "\n", " all_completed = True\n", " any_failed = False\n", "\n", " for t in my_tasks:\n", " name = t.config['description']\n", " status = t.state\n", "\n", " # CRASH PROOF FIX:\n", " # We assume filename matches the Task Description (which we set up to be true)\n", " fname = name + '.csv'\n", " fpath = FOLDER + fname\n", "\n", " # Check size if exists\n", " if os.path.exists(fpath):\n", " size_mb = os.path.getsize(fpath) / (1024 * 1024)\n", " file_status = f\"YES ({size_mb:.1f} MB)\"\n", " else:\n", " file_status = \"NO\"\n", "\n", " print(f\"{name:<25} | {status:<12} | {file_status}\")\n", "\n", " if status != 'COMPLETED':\n", " all_completed = False\n", " if status == 'FAILED':\n", " any_failed = True\n", " # Safe error retrieval\n", " err = t.status().get('error_message', 'Unknown Error')\n", " print(f\" Error: {err}\")\n", "\n", " # SUCCESS CHECK\n", " # We need at least 4 COMPLETED tasks (S1_Batch_A, S2_Batch_A, S1_Batch_B, S2_Batch_B)\n", " completed_count = sum(1 for t in my_tasks if t.state == 'COMPLETED')\n", "\n", " if completed_count >= 4 and all_completed:\n", " print(\"\\n SUCCESS! All batches are finished.\")\n", " print(\"You can now proceed to Part 2 (The Merge).\")\n", " break\n", "\n", " if any_failed:\n", " print(\"\\n FAILURE DETECTED. Please check the error message above.\")\n", " break\n", "\n", " time.sleep(60)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "Zp20vkXwzzqc", "executionInfo": { "status": "error", "timestamp": 1769717944583, "user_tz": -330, "elapsed": 9546465, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "4d75778b-6240-4910-d2b0-ba0f429a1baf" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "--- DEEP SCAN TASK MONITOR (FIXED) ---\n", "\n", "Time: 17:39:58\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | READY | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:40:58\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | READY | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:41:58\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | READY | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:42:58\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | READY | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:43:58\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | READY | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:44:58\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | READY | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCEL_REQUESTED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:45:59\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:46:59\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:47:59\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:48:59\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:49:59\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:50:59\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:52:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:53:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:54:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:55:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:56:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:57:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:58:00\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 17:59:01\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | READY | NO\n", "Wheat_S1_Batch_A | RUNNING | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:00:01\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:01:01\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:02:01\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:03:01\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:04:01\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:05:02\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:06:02\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:07:02\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:08:02\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:09:02\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:10:02\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:11:03\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:12:03\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:13:03\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:14:03\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:15:03\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:16:03\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:17:04\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:18:04\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:19:04\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:20:04\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:21:05\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:22:05\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:23:05\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | READY | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:24:05\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:25:05\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:26:05\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:27:06\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Time: 18:28:06\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:29:07\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:30:07\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:31:07\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:32:07\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:33:07\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:34:07\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:35:08\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:36:08\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:37:08\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:38:08\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:39:08\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:40:08\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:41:09\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:42:09\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:43:09\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:44:09\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:45:09\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:46:09\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:47:10\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:48:10\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:49:10\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:50:10\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:51:10\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:52:11\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:53:11\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:54:11\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:55:11\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:56:11\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | NO\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:57:11\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:58:12\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 18:59:12\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:00:12\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:01:12\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:02:13\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:03:13\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:04:13\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:05:13\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:06:13\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:07:13\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:08:14\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:09:14\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:10:14\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:11:14\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:12:14\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:13:14\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:14:15\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:15:15\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:16:15\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:17:15\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:18:15\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:19:15\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:20:16\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:21:16\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:22:16\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:23:16\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:24:16\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Time: 19:25:17\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:26:17\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:27:17\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:28:18\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:29:18\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:30:18\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:31:18\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:32:18\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:33:18\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:34:19\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:35:19\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:36:19\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:37:19\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:38:19\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:39:19\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:40:20\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:41:20\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:42:20\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:43:20\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:44:20\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:45:20\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:46:21\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:47:21\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:48:21\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:49:21\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:50:21\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:51:21\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:52:22\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:53:22\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:54:22\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:55:22\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:56:22\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:57:22\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:58:23\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 19:59:23\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:00:23\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:01:23\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:02:23\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:03:23\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:04:24\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:05:24\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:06:24\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:07:24\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:08:24\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:09:25\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:10:25\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:11:25\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:12:25\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:13:25\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:14:25\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:15:26\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:16:26\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:17:26\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "\n", "Time: 20:18:26\n", "TASK NAME | STATUS | DRIVE FILE\n", "------------------------------------------------------------\n", "Wheat_S2_Batch_B | COMPLETED | YES (57.1 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (41.2 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (15.5 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-1927954896.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import os\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "print(\"--- CSV INSPECTOR ---\")\n", "try:\n", " # Load just the first 5 rows of Batch A (Radar)\n", " test_path = INPUT_DIR + 'Wheat_S1_Batch_A.csv'\n", " df_test = pd.read_csv(test_path, nrows=5)\n", "\n", " print(f\"\\nFile: {test_path}\")\n", " print(\"Found Columns:\")\n", " print(df_test.columns.tolist())\n", "\n", " print(\"\\nFirst 2 Rows of Data:\")\n", " print(df_test.head(2))\n", "\n", "except FileNotFoundError:\n", " print(f\"Could not find {test_path}\")\n", "except Exception as e:\n", " print(f\" Error reading file: {e}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 260 }, "id": "jAYHft-saOTN", "executionInfo": { "status": "ok", "timestamp": 1769718072440, "user_tz": -330, "elapsed": 43, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "cff179ae-8a88-452e-dd40-a63702c1a57f" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "--- CSV INSPECTOR ---\n", "\n", "File: /content/drive/MyDrive/LSTM_Wheat_Results/Wheat_S1_Batch_A.csv\n", "Found Columns:\n", "['system:index', 'class', 'date', 'sensor_type', '.geo']\n", "\n", "First 2 Rows of Data:\n", " system:index class date sensor_type \\\n", "0 0_1_0 1 2023-10-02 S1 \n", "1 0_1_1 1 2023-10-02 S1 \n", "\n", " .geo \n", "0 {\"type\":\"MultiPoint\",\"coordinates\":[]} \n", "1 {\"type\":\"MultiPoint\",\"coordinates\":[]} \n" ] } ] }, { "cell_type": "code", "source": [ "\n", "# PART 2: MERGE BATCHES & PROCESS\n", "\n", "import pandas as pd\n", "import numpy as np\n", "from scipy.interpolate import interp1d\n", "from scipy.signal import savgol_filter\n", "import os\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "COMMON_START = pd.Timestamp('2023-10-01')\n", "COMMON_END = pd.Timestamp('2024-04-30')\n", "\n", "# --- 1. GENERATE TARGET DATES (Exact Logic from Old Code) ---\n", "TARGET_DATES = []\n", "curr = COMMON_START\n", "while curr <= COMMON_END:\n", " if curr.day == 1 or curr.day == 16:\n", " TARGET_DATES.append(curr)\n", " curr += pd.Timedelta(days=1)\n", "TARGET_DAYS_INT = [(t - COMMON_START).days for t in TARGET_DATES]\n", "print(f\"Target Time Steps: {len(TARGET_DATES)}\") # Should be 14\n", "\n", "# --- 2. HELPER: SPIKE REMOVAL (Exact Logic) ---\n", "def remove_spikes(series, threshold=0.4):\n", " \"\"\"\n", " Removes sudden jumps > 0.4 in Optical data.\n", " \"\"\"\n", " diff = series.diff().abs()\n", " mask = (diff > threshold)\n", " series_clean = series.copy()\n", " series_clean[mask] = np.nan\n", " return series_clean\n", "\n", "print(\"3. Loading & Merging Batches...\")\n", "try:\n", " # Load all 4 batch files\n", " s1_a = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_A.csv')\n", " s2_a = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_A.csv')\n", " s1_b = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_B.csv')\n", " s2_b = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_B.csv')\n", " print(\" All 4 batch files loaded.\")\n", "\n", " # Stack Batches (A + B)\n", " df_s1 = pd.concat([s1_a, s1_b], ignore_index=True)\n", " df_s2 = pd.concat([s2_a, s2_b], ignore_index=True)\n", "\n", " # Clean IDs (Exact Logic)\n", " df_s1['unique_id'] = df_s1['system:index'].apply(lambda x: x.split('_')[-1])\n", " df_s2['unique_id'] = df_s2['system:index'].apply(lambda x: x.split('_')[-1])\n", "\n", " # Master Merge\n", " df = pd.concat([df_s1, df_s2], ignore_index=True)\n", " df['date'] = pd.to_datetime(df['date'])\n", "\n", "except FileNotFoundError:\n", " print(\" ERROR: Files missing. Please wait for GEE tasks to finish.\")\n", " raise\n", "\n", "# --- 3. METADATA & CONVERSION ---\n", "meta_df = df[['unique_id', 'class']].drop_duplicates()\n", "\n", "# dB to Linear Conversion (Exact Logic)\n", "print(\" Converting dB to Linear...\")\n", "for b in ['VV', 'VH']:\n", " mask = (df['sensor_type'] == 'S1') & (df[b].notna())\n", " df.loc[mask, b] = 10**(df.loc[mask, b]/10.0)\n", "\n", "# --- 4. MAIN PROCESSING LOOP ---\n", "X_list, y_list = [], []\n", "grouped = df.groupby('unique_id')\n", "\n", "print(f\"4. Processing {len(grouped)} points...\")\n", "\n", "count = 0\n", "for pid, group in grouped:\n", " # Get Label\n", " try: label = meta_df[meta_df['unique_id'] == pid]['class'].iloc[0]\n", " except: continue\n", "\n", " # 80% Rule (Exact Logic)\n", " if len(group[group['sensor_type'] == 'S2']) < 5: continue\n", "\n", " p_mat, valid = [], True\n", " bands = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']\n", "\n", " for band in bands:\n", " ts = group[['date', band]].dropna().sort_values('date')\n", "\n", " # Spike Removal (Optical Only)\n", " if band.startswith('B'):\n", " ts[band] = remove_spikes(ts[band], threshold=0.4)\n", " ts = ts.dropna()\n", "\n", " # Check Length\n", " if len(ts) < 2:\n", " valid = False; break\n", "\n", " # Interpolation (Exact Logic)\n", " days = (ts['date'] - COMMON_START).dt.days.values\n", " vals = ts[band].values\n", " f = interp1d(days, vals, kind='linear', fill_value='extrapolate')\n", " res = f(TARGET_DAYS_INT)\n", "\n", " # SavGol Filter (Exact Logic)\n", " window = 5\n", " if len(res) < window: window = 3\n", " try: smoothed = savgol_filter(res, window, 2)\n", " except: smoothed = res\n", "\n", " p_mat.append(smoothed)\n", "\n", " if valid:\n", " X_list.append(np.array(p_mat).T)\n", " y_list.append(label)\n", " count += 1\n", "\n", " if count % 1000 == 0: print(f\" Processed {count}...\")\n", "\n", "# --- 5. SAVE ---\n", "X_data, y_data = np.array(X_list), np.array(y_list)\n", "np.save(INPUT_DIR + 'X_wheat.npy', X_data)\n", "np.save(INPUT_DIR + 'y_wheat.npy', y_data)\n", "\n", "print(f\"\\n PROCESSING COMPLETE.\")\n", "print(f\"Final Data Shape: {X_data.shape}\")\n", "print(f\"Labels Shape: {y_data.shape}\")" ], "metadata": { "id": "N_FE1o2vqqnJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# PART 3: LSTM TRAINING\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.utils.data import Dataset, DataLoader, random_split\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "\n", "# LOAD DATA\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "X = np.load(INPUT_DIR + 'X_wheat.npy')\n", "y = np.load(INPUT_DIR + 'y_wheat.npy')\n", "\n", "# HYPERPARAMETERS\n", "INPUT_DIM = 12\n", "HIDDEN_DIM = 64\n", "LAYERS = 2\n", "EPOCHS = 200\n", "BATCH_SIZE = 32\n", "LR = 0.001\n", "\n", "# 1. DATASET\n", "class WheatDataset(Dataset):\n", " def __init__(self, X, y):\n", " self.X = torch.tensor(X, dtype=torch.float32)\n", " self.y = torch.tensor(y, dtype=torch.float32).unsqueeze(1)\n", " def __len__(self): return len(self.X)\n", " def __getitem__(self, i): return self.X[i], self.y[i]\n", "\n", "dataset = WheatDataset(X, y)\n", "train_len = int(0.8 * len(dataset))\n", "train_set, val_set = random_split(dataset, [train_len, len(dataset)-train_len])\n", "train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)\n", "val_loader = DataLoader(val_set, batch_size=BATCH_SIZE)\n", "\n", "# 2. MODEL\n", "class WheatLSTM(nn.Module):\n", " def __init__(self):\n", " super(WheatLSTM, self).__init__()\n", " # batch_first=True -> (Batch, Seq, Feat)\n", " self.lstm = nn.LSTM(INPUT_DIM, HIDDEN_DIM, LAYERS, batch_first=True, dropout=0.2)\n", " self.fc = nn.Linear(HIDDEN_DIM, 1) # Output 1 score\n", " self.sigmoid = nn.Sigmoid()\n", "\n", " def forward(self, x):\n", " # x shape: (Batch, 14, 12)\n", " # out shape: (Batch, 14, 64)\n", " out, _ = self.lstm(x)\n", " # We only want the LAST time step (the end of the season)\n", " last_step = out[:, -1, :]\n", " prediction = self.sigmoid(self.fc(last_step))\n", " return prediction\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "model = WheatLSTM().to(device)\n", "criterion = nn.BCELoss()\n", "optimizer = optim.Adam(model.parameters(), lr=LR)\n", "\n", "# 3. TRAIN LOOP\n", "train_losses = []\n", "val_accuracies = []\n", "\n", "# Ensure checkpoint folder exists (optional specific folder)\n", "CKPT_DIR = INPUT_DIR + 'checkpoints/'\n", "if not os.path.exists(CKPT_DIR): os.makedirs(CKPT_DIR)\n", "\n", "print(\"Starting Training...\")\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " batch_loss = 0\n", " for X_batch, y_batch in train_loader:\n", " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", "\n", " optimizer.zero_grad()\n", " y_pred = model(X_batch)\n", " loss = criterion(y_pred, y_batch)\n", " loss.backward()\n", " optimizer.step()\n", " batch_loss += loss.item()\n", "\n", " # Validation\n", " model.eval()\n", " correct = 0\n", " total = 0\n", " with torch.no_grad():\n", " for X_val, y_val in val_loader:\n", " X_val, y_val = X_val.to(device), y_val.to(device)\n", " preds = model(X_val)\n", " predicted_class = (preds > 0.5).float()\n", " correct += (predicted_class == y_val).sum().item()\n", " total += y_val.size(0)\n", "\n", " val_acc = correct / total\n", " train_losses.append(batch_loss/len(train_loader))\n", " val_accuracies.append(val_acc)\n", "\n", " # Print Progress\n", " if (epoch + 1) % 10 == 0:\n", " print(f\"Epoch {epoch+1}: Loss={batch_loss/len(train_loader):.4f} | Val Acc={val_acc*100:.2f}%\")\n", "\n", "\n", " if (epoch + 1) % 5 == 0:\n", " ckpt_path = CKPT_DIR + f'wheat_lstm_epoch_{epoch+1}.pth'\n", " torch.save({\n", " 'epoch': epoch + 1,\n", " 'model_state_dict': model.state_dict(),\n", " 'optimizer_state_dict': optimizer.state_dict(),\n", " 'loss': batch_loss/len(train_loader),\n", " 'val_acc': val_acc\n", " }, ckpt_path)\n", " print(f\" -> Checkpoint saved: {ckpt_path}\")\n", "\n", "# 4. PLOT\n", "plt.plot(train_losses)\n", "plt.title(\"Training Loss\")\n", "plt.show()\n", "\n", "# SAVE FINAL MODEL\n", "torch.save(model.state_dict(), INPUT_DIR + 'wheat_lstm_final.pth')\n", "print(\"Final Model Saved.\")" ], "metadata": { "id": "-WJ0rXGpqzUx" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# PART 1: SCIENTIFIC GEE EXTRACTION\n", "\n", "import ee\n", "import os\n", "from google.colab import drive\n", "\n", "# 1. SETUP\n", "drive.mount('/content/drive', force_remount=True)\n", "try:\n", " ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n", "except:\n", " ee.Authenticate()\n", " ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n", "\n", "# CONFIG\n", "WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'\n", "START_DATE = '2023-10-01'\n", "END_DATE = '2024-04-30'\n", "BATCHES = [{'name': 'Batch_A', 'seed': 42}, {'name': 'Batch_B', 'seed': 123}]\n", "POINTS_PER_BATCH = 5000\n", "SPLIT = POINTS_PER_BATCH // 2\n", "\n", "# --- 2. SCIENTIFIC FUNCTIONS ----\n", "def apply_lee_filter_safe(image):\n", " def lee_single(b):\n", " img_band = image.select(b)\n", " mean = img_band.reduceNeighborhood(ee.Reducer.mean(), ee.Kernel.square(3))\n", " variance = img_band.reduceNeighborhood(ee.Reducer.variance(), ee.Kernel.square(3))\n", " overall_var_img = ee.Image.constant(0.004) # Standard scientific constant\n", " k = variance.divide(variance.add(overall_var_img))\n", " return mean.add(k.multiply(img_band.subtract(mean))).rename(b)\n", " return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)\n", "\n", "def maskS2clouds(image):\n", " qa = image.select('QA60')\n", " mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))\n", " return image.updateMask(mask).select(['B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']) \\\n", " .copyProperties(image, [\"system:time_start\"])\n", "\n", "def make_semimonthly_composites(collection):\n", " \"\"\"Scientific Aggregation Logic: 15-day median binned steps.\"\"\"\n", " start = ee.Date(START_DATE)\n", " end = ee.Date(END_DATE)\n", " n_months = end.difference(start, 'month').round()\n", "\n", " def create_steps(m):\n", " m_start = start.advance(m, 'month')\n", " mid_month = m_start.advance(15, 'day')\n", " next_month = m_start.advance(1, 'month')\n", "\n", " # 1st-15th Median\n", " img1 = collection.filterDate(m_start, mid_month).median() \\\n", " .set('system:time_start', m_start.millis())\n", " # 16th-End Median\n", " img2 = collection.filterDate(mid_month, next_month).median() \\\n", " .set('system:time_start', mid_month.millis())\n", " return ee.List([img1, img2])\n", "\n", " steps = ee.List.sequence(0, n_months.subtract(1)).map(create_steps).flatten()\n", " return ee.ImageCollection.fromImages(steps)\n", "\n", "# --- 3. EXECUTION ---\n", "wheat_mask = ee.Image(WHEAT_MASK_ASSET)\n", "bounds = wheat_mask.geometry()\n", "\n", "for batch in BATCHES:\n", " b_name = batch['name']\n", " print(f\"\\n SUBMITTING {b_name} (Strict Scientific Mode)...\")\n", "\n", " # BALANCED SAMPLING (Wheat & Non-Wheat)\n", " w_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']) \\\n", " .filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 1))\n", " n_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']+99) \\\n", " .filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 0))\n", " points = w_pts.merge(n_pts)\n", "\n", " # Collections\n", " s1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.eq('instrumentMode', 'IW')).map(apply_lee_filter_safe)\n", " s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)).map(maskS2clouds)\n", "\n", " # Server-Side Compositing (Refrence Gate)\n", " agg_s1 = make_semimonthly_composites(s1.select(['VV', 'VH']))\n", " agg_s2 = make_semimonthly_composites(s2)\n", "\n", " def extract(img, prefix, bands):\n", " return img.select(bands).reduceRegions(collection=points, reducer=ee.Reducer.first(), scale=10, tileScale=16) \\\n", " .map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))\n", "\n", " # Strict Exports (Forcing Columns)\n", " ee.batch.Export.table.toDrive(\n", " collection=agg_s1.map(lambda i: extract(i, 'S1', ['VV', 'VH'])).flatten(),\n", " description=f'Wheat_S1_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S1_{b_name}',\n", " fileFormat='CSV', selectors=['system:index', 'class', 'date', 'sensor_type', 'VV', 'VH']\n", " ).start()\n", "\n", " ee.batch.Export.table.toDrive(\n", " collection=agg_s2.map(lambda i: extract(i, 'S2', ['B2','B3','B4','B5','B6','B7','B8','B8A', 'B11', 'B12'])).flatten(),\n", " description=f'Wheat_S2_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S2_{b_name}',\n", " fileFormat='CSV', selectors=['system:index', 'class', 'date', 'sensor_type', 'B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']\n", " ).start()\n", "\n", "print(\"Tasks submitted. Wait for COMPLETED status.\")" ], "metadata": { "id": "t368BhZP-A1U" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# PART 2: THE REFINEMENT GATE\n", "import pandas as pd\n", "import numpy as np\n", "from scipy.interpolate import interp1d\n", "from scipy.signal import savgol_filter\n", "from tqdm.notebook import tqdm\n", "import os\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "COMMON_START = pd.Timestamp('2023-10-01')\n", "# Exact 14 timestamps (1st and 15th)\n", "TARGET_DATES = [COMMON_START + pd.Timedelta(days=d) for d in range(213) if (COMMON_START + pd.Timedelta(days=d)).day in [1, 15]]\n", "TARGET_DAYS_INT = [(t - COMMON_START).days for t in TARGET_DATES]\n", "\n", "def remove_spikes(series):\n", " # Reference Spike Logic (threshold 0.4)\n", " diff = series.diff().abs()\n", " series[diff > 0.4] = np.nan\n", " return series\n", "\n", "print(\"1. Loading Data (0 to NaN Trick)...\")\n", "# na_values=0 ensures that zeros are treated as \"No Data\" during interpolation\n", "s1_a = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_A.csv', na_values=0)\n", "s2_a = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_A.csv', na_values=0)\n", "s1_b = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_B.csv', na_values=0)\n", "s2_b = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_B.csv', na_values=0)\n", "\n", "df = pd.concat([s1_a, s2_a, s1_b, s2_b], ignore_index=True)\n", "df['unique_id'] = df['system:index'].apply(lambda x: x.split('_')[-1])\n", "df['date'] = pd.to_datetime(df['date'])\n", "\n", "# Conversion Gate (Linear Scale)\n", "for b in ['VV', 'VH']:\n", " mask = (df['sensor_type'] == 'S1') & (df[b].notna())\n", " df.loc[mask, b] = 10**(df.loc[mask, b]/10.0)\n", "\n", "X_list, y_list = [], []\n", "grouped = df.groupby('unique_id')\n", "\n", "print(f\"2. Applying 80% Filter & SavGol Smoothing...\")\n", "for pid, group in tqdm(grouped, total=len(grouped)):\n", " label = group['class'].iloc[0]\n", "\n", "\n", " if len(group[group['sensor_type'] == 'S2'].dropna(subset=['B2'])) < 5: continue\n", "\n", " p_mat, valid = [], True\n", " bands = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']\n", "\n", " for band in bands:\n", " ts = group[['date', band]].dropna().sort_values('date')\n", " if band.startswith('B'): ts[band] = remove_spikes(ts[band])\n", " ts = ts.dropna()\n", "\n", " if len(ts) < 2:\n", " valid = False; break\n", "\n", " # Interpolation Gate\n", " f = interp1d((ts['date'] - COMMON_START).dt.days.values, ts[band].values, kind='linear', fill_value='extrapolate')\n", " res = f(TARGET_DAYS_INT)\n", "\n", " # REFINEMENT GATE: Savitzky-Golay (Window 5, Poly 2)\n", " try: smoothed = savgol_filter(res, 5, 2)\n", " except: smoothed = res\n", "\n", " p_mat.append(smoothed)\n", "\n", " if valid:\n", " X_list.append(np.array(p_mat).T)\n", " y_list.append(label)\n", "\n", "X_data, y_data = np.array(X_list), np.array(y_list)\n", "np.save(INPUT_DIR + 'X_wheat.npy', X_data)\n", "np.save(INPUT_DIR + 'y_wheat.npy', y_data)\n", "print(f\" SUCCESS: Dataset saved. Shape: {X_data.shape}\")" ], "metadata": { "id": "_cvTVwF6-Co-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# PART 3: LSTM TRAINING\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n", "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "import os\n", "\n", "# 1. LOAD DATA\n", "DATA_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "X = np.load(DATA_DIR + 'X_wheat.npy')\n", "y = np.load(DATA_DIR + 'y_wheat.npy')\n", "\n", "print(f\"Loaded Data: {X.shape}, Labels: {y.shape}\")\n", "\n", "# 2. STANDARDIZATION (CRITICAL FOR LSTM)\n", "# We flatten to (N*T, F) to fit the scaler, then reshape back\n", "N, T, F = X.shape\n", "X_flat = X.reshape(-1, F)\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X_flat).reshape(N, T, F)\n", "\n", "# 3. TRAIN-TEST SPLIT\n", "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n", "\n", "# 4. DEFINE ARCHITECTURE\n", "model = Sequential([\n", " # Layer 1: Captures initial temporal patterns\n", " LSTM(128, input_shape=(T, F), return_sequences=True),\n", " BatchNormalization(),\n", " Dropout(0.2),\n", "\n", " # Layer 2: Deep temporal reasoning\n", " LSTM(64),\n", " BatchNormalization(),\n", " Dropout(0.2),\n", "\n", " # Decision Head\n", " Dense(32, activation='relu'),\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", "model.summary()\n", "\n", "# 5. CHECKPOINT & CALLBACKS\n", "checkpoint_path = DATA_DIR + \"checkpoints/wheat_model_epoch_{epoch:02d}.h5\"\n", "if not os.path.exists(DATA_DIR + \"checkpoints/\"):\n", " os.makedirs(DATA_DIR + \"checkpoints/\")\n", "\n", "# Save every 5 epochs\n", "checkpoint_callback = ModelCheckpoint(\n", " filepath=checkpoint_path,\n", " save_weights_only=False,\n", " monitor='val_accuracy',\n", " mode='max',\n", " save_freq=5 * (len(X_train) // 32)\n", ")\n", "\n", "# Early stopping to save time if model converges early\n", "early_stop = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n", "\n", "# 6. TRAIN\n", "print(\"\\n Starting Training for 200 Epochs...\")\n", "history = model.fit(\n", " X_train, y_train,\n", " validation_data=(X_test, y_test),\n", " epochs=200,\n", " batch_size=32,\n", " callbacks=[checkpoint_callback, early_stop],\n", " verbose=1\n", ")\n", "\n", "# 7. SAVE FINAL MODEL\n", "model.save(DATA_DIR + 'wheat_lstm_final.h5')\n", "print(f\"\\n Training Complete. Final model saved to: {DATA_DIR}wheat_lstm_final.h5\")" ], "metadata": { "id": "7kdit1El-mEz" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "---------------------------------------------------------NEW updated CODE--------------------------------------------------------------------------------------\n" ], "metadata": { "id": "3eGo4Z4ihlxM" } }, { "cell_type": "code", "source": [ "\n", "# PART 1: SCIENTIFIC GEE EXTRACTION\n", "import ee\n", "import os\n", "from google.colab import drive\n", "\n", "# 1. SETUP\n", "drive.mount('/content/drive', force_remount=True)\n", "try:\n", " ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n", "except:\n", " ee.Authenticate()\n", " ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n", "\n", "# CONFIG\n", "WHEAT_MASK_ASSET = '[REDACTED_FOR_SECURITY]'\n", "START_DATE = '2023-10-01'\n", "END_DATE = '2024-04-30'\n", "BATCHES = [{'name': 'Batch_A', 'seed': 42}, {'name': 'Batch_B', 'seed': 123}]\n", "POINTS_PER_BATCH = 5000\n", "SPLIT = POINTS_PER_BATCH // 2\n", "\n", "\n", "def apply_lee_filter_safe(image):\n", "\n", " def lee_single(b):\n", " img_band = image.select(b)\n", " mean = img_band.reduceNeighborhood(ee.Reducer.mean(), ee.Kernel.square(3))\n", " variance = img_band.reduceNeighborhood(ee.Reducer.variance(), ee.Kernel.square(3))\n", " overall_var_img = ee.Image.constant(0.004)\n", " k = variance.divide(variance.add(overall_var_img))\n", " return mean.add(k.multiply(img_band.subtract(mean))).rename(b)\n", " return image.addBands(lee_single('VV'), overwrite=True).addBands(lee_single('VH'), overwrite=True)\n", "\n", "def maskS2clouds(image):\n", "\n", " qa = image.select('QA60')\n", " mask = qa.bitwiseAnd(1<<10).eq(0).And(qa.bitwiseAnd(1<<11).eq(0))\n", " return image.updateMask(mask).select(['B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']) \\\n", " .copyProperties(image, [\"system:time_start\"])\n", "\n", "def make_semimonthly_composites(collection):\n", " \"\"\"\n", " Aggregation: 15-day median binned steps.\n", "\n", " \"\"\"\n", " start = ee.Date(START_DATE)\n", " end = ee.Date(END_DATE)\n", " n_months = end.difference(start, 'month').round()\n", "\n", " def create_steps(m):\n", " m_start = start.advance(m, 'month')\n", " mid_month = m_start.advance(15, 'day')\n", " next_month = m_start.advance(1, 'month')\n", "\n", " # 1st-15th Median\n", " img1 = collection.filterDate(m_start, mid_month).median() \\\n", " .set('system:time_start', m_start.millis())\n", " # 16th-End Median\n", " img2 = collection.filterDate(mid_month, next_month).median() \\\n", " .set('system:time_start', mid_month.millis())\n", " return ee.List([img1, img2])\n", "\n", " steps = ee.List.sequence(0, n_months.subtract(1)).map(create_steps).flatten()\n", " return ee.ImageCollection.fromImages(steps)\n", "\n", "# --- 3. EXECUTION ---\n", "wheat_mask = ee.Image(WHEAT_MASK_ASSET)\n", "bounds = wheat_mask.geometry()\n", "\n", "# Cancel old tasks to keep queue clean\n", "tasks = ee.batch.Task.list()\n", "for t in tasks:\n", " if t.state in ['READY', 'RUNNING'] and 'Wheat' in t.config['description']:\n", " ee.data.cancelTask(t.id)\n", "\n", "for batch in BATCHES:\n", " b_name = batch['name']\n", " print(f\"\\n SUBMITTING {b_name} (Strict Scientific Mode)...\")\n", "\n", " # BALANCED SAMPLING\n", " w_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']) \\\n", " .filter(ee.Filter.bounds(wheat_mask.updateMask(wheat_mask.eq(1)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 1))\n", " n_pts = ee.FeatureCollection.randomPoints(bounds, SPLIT*2, batch['seed']+99) \\\n", " .filter(ee.Filter.bounds(wheat_mask.eq(0).updateMask(wheat_mask.eq(0)).geometry())).limit(SPLIT).map(lambda f: f.set('class', 0))\n", " points = w_pts.merge(n_pts)\n", "\n", " # Collections\n", " s1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.eq('instrumentMode', 'IW')).map(apply_lee_filter_safe)\n", " s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterDate(START_DATE, END_DATE).filterBounds(bounds) \\\n", " .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 60)).map(maskS2clouds)\n", "\n", " # Server-Side Compositing (The \"Median\" Step)\n", " agg_s1 = make_semimonthly_composites(s1.select(['VV', 'VH']))\n", " agg_s2 = make_semimonthly_composites(s2)\n", "\n", " def extract(img, prefix, bands):\n", " # Uses tileScale=16\n", " return img.select(bands).reduceRegions(collection=points, reducer=ee.Reducer.first(), scale=10, tileScale=16) \\\n", " .map(lambda f: f.set('date', img.date().format('YYYY-MM-dd')).set('sensor_type', prefix))\n", "\n", " # Strict Exports (With Selectors)\n", " ee.batch.Export.table.toDrive(\n", " collection=agg_s1.map(lambda i: extract(i, 'S1', ['VV', 'VH'])).flatten(),\n", " description=f'Wheat_S1_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S1_{b_name}',\n", " fileFormat='CSV',\n", " selectors=['system:index', 'class', 'date', 'sensor_type', 'VV', 'VH']\n", " ).start()\n", "\n", " ee.batch.Export.table.toDrive(\n", " collection=agg_s2.map(lambda i: extract(i, 'S2', ['B2','B3','B4','B5','B6','B7','B8','B8A', 'B11', 'B12'])).flatten(),\n", " description=f'Wheat_S2_{b_name}', folder='LSTM_Wheat_Results', fileNamePrefix=f'Wheat_S2_{b_name}',\n", " fileFormat='CSV',\n", " selectors=['system:index', 'class', 'date', 'sensor_type', 'B2','B3','B4','B5','B6','B7','B8','B8A','B11','B12']\n", " ).start()\n", "\n", "print(\"Tasks submitted.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_FniUpUrhgb7", "executionInfo": { "status": "ok", "timestamp": 1769837385204, "user_tz": -330, "elapsed": 59413, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "fe7dab29-af1a-4e5b-ca64-373c25cd6610" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n", "\n", " SUBMITTING Batch_A (Strict Scientific Mode)...\n", "\n", " SUBMITTING Batch_B (Strict Scientific Mode)...\n", "Tasks submitted.\n" ] } ] }, { "cell_type": "code", "source": [ "import time\n", "import os\n", "import ee\n", "from google.colab import drive\n", "\n", "# Ensure Drive is accessible for file checking\n", "if not os.path.exists('/content/drive'):\n", " drive.mount('/content/drive')\n", "\n", "FOLDER = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "print(\"--- DEEP SCAN TASK MONITOR (SCIENTIFIC MODE) ---\")\n", "\n", "while True:\n", " try:\n", " # Fetch latest task list\n", " tasks = ee.batch.Task.list()\n", " except Exception as e:\n", " print(f\"Connection glitch ({e}), retrying in 10s...\")\n", " time.sleep(10)\n", " continue\n", "\n", "\n", " # We take the top 20 to ensure we catch all active ones\n", " my_tasks = [t for t in tasks[:20] if 'Wheat' in t.config['description']]\n", "\n", " # Sort them so S1/S2 and Batch A/B appear in a consistent order\n", " my_tasks.sort(key=lambda t: t.config['description'])\n", "\n", " print(f\"\\nTime: {time.strftime('%H:%M:%S')}\")\n", " print(f\"{'TASK NAME':<30} | {'STATUS':<12} | {'DRIVE FILE (MB)'}\")\n", " print(\"-\" * 70)\n", "\n", " all_completed = True\n", " any_failed = False\n", " completed_count = 0\n", "\n", " for t in my_tasks:\n", " name = t.config['description']\n", " status = t.state\n", "\n", " # Construct expected filename matches the export code\n", " # Logic: description 'Wheat_S1_Batch_A' -> filename 'Wheat_S1_Batch_A.csv'\n", " fname = name + '.csv'\n", " fpath = FOLDER + fname\n", "\n", " # Check if file exists in Drive and get size\n", " if os.path.exists(fpath):\n", " size_mb = os.path.getsize(fpath) / (1024 * 1024)\n", " file_status = f\"YES ({size_mb:.1f} MB)\"\n", " else:\n", " file_status = \"NO\"\n", "\n", " print(f\"{name:<30} | {status:<12} | {file_status}\")\n", "\n", " # Logic Checks\n", " if status == 'COMPLETED':\n", " completed_count += 1\n", " else:\n", " all_completed = False\n", "\n", " if status == 'FAILED':\n", " any_failed = True\n", " err = t.status().get('error_message', 'Unknown Error')\n", " print(f\" ERROR: {err}\")\n", "\n", "\n", " if completed_count >= 4 and all_completed:\n", " print(\"\\n SUCCESS! All 4 scientific batches are finished.\")\n", " print(\"You can now proceed to Part 2 (The Refinement/Merge).\")\n", " break\n", "\n", " if any_failed:\n", " print(\"\\n FAILURE DETECTED. Please check the error message above.\")\n", " break\n", "\n", " # Refresh every 60 seconds\n", " time.sleep(60)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "Qvc_ES2hiWY2", "executionInfo": { "status": "error", "timestamp": 1769839785322, "user_tz": -330, "elapsed": 2263270, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "c6bfb388-3ae8-40c5-c9ee-80ae4c8003d6" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--- DEEP SCAN TASK MONITOR (SCIENTIFIC MODE) ---\n", "\n", "Time: 05:32:02\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | RUNNING | NO\n", "Wheat_S1_Batch_B | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | READY | NO\n", "Wheat_S2_Batch_B | COMPLETED | NO\n", "\n", "Time: 05:33:02\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_B | COMPLETED | NO\n", "\n", "Time: 05:34:02\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_B | COMPLETED | NO\n", "\n", "Time: 05:35:02\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | RUNNING | NO\n", "Wheat_S2_Batch_B | COMPLETED | NO\n", "\n", "Time: 05:36:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:37:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:38:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:39:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:40:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:41:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:42:03\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:43:04\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:44:04\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:45:04\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | RUNNING | NO\n", "Wheat_S2_Batch_A | COMPLETED | NO\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:46:04\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:47:04\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:48:05\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:49:05\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:50:05\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:51:05\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:52:05\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:53:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:54:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:55:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:56:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:57:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:58:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 05:59:06\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:00:07\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:01:07\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:02:07\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:03:07\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:04:08\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:05:08\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:06:08\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:07:08\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:08:08\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "\n", "Time: 06:09:08\n", "TASK NAME | STATUS | DRIVE FILE (MB)\n", "----------------------------------------------------------------------\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_A | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S1_Batch_B | COMPLETED | YES (4.3 MB)\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | CANCELLED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_2023-10-01_2024-04-30 | COMPLETED | NO\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_A | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n", "Wheat_S2_Batch_B | COMPLETED | YES (5.9 MB)\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-516124977.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;31m# Refresh every 60 seconds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import pandas as pd\n", "import glob\n", "\n", "# CONFIG\n", "FOLDER = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "print(f\" INSPECTING FOLDER: {FOLDER}\\n\")\n", "\n", "# 1. FIND FILES\n", "files = sorted(glob.glob(FOLDER + \"*.csv\"))\n", "\n", "if not files:\n", " print(\" NO FILES FOUND. Please wait for GEE tasks to finish.\")\n", "else:\n", " print(f\" Found {len(files)} CSV files.\\n\")\n", " print(f\"{'FILENAME':<40} | {'SIZE (MB)':<10} | {'STATUS'} | {'COLUMNS CHECK'}\")\n", " print(\"-\" * 110)\n", "\n", " for f in files:\n", " # Get Size\n", " size_mb = os.path.getsize(f) / (1024 * 1024)\n", " name = os.path.basename(f)\n", "\n", " # Check Status based on size\n", " status = \" OK\"\n", " if size_mb < 0.01: status = \" EMPTY\"\n", " elif size_mb < 5: status = \" SMALL\"\n", "\n", " # Peek Inside (Check Columns)\n", " try:\n", " df = pd.read_csv(f, nrows=2)\n", " cols = df.columns.tolist()\n", "\n", " # Check for Critical Data Columns\n", " if 'S1' in name:\n", " has_data = 'VV' in cols and 'VH' in cols\n", " col_status = \" S1 Data Found\" if has_data else \" MISSING VV/VH\"\n", " elif 'S2' in name:\n", " has_data = 'B2' in cols\n", " col_status = \" S2 Data Found\" if has_data else \" MISSING BANDS\"\n", " else:\n", " col_status = \" Unknown Type\"\n", "\n", " except Exception as e:\n", " col_status = f\" Error reading: {str(e)}\"\n", "\n", " print(f\"{name:<40} | {size_mb:<10.2f} | {status:<6} | {col_status}\")\n", "\n", "print(\"\\n--------------------------------------------------------------------------------------------------------------\")\n", "print(\"GUIDE:\")\n", "print(\"1. Sizes: S1 files should be ~10-15 MB. S2 files should be ~40-60 MB.\")\n", "print(\"2. Columns: You MUST see ' Data Found'. If you see ' MISSING', the selectors failed.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Jk-uI4K3iG3L", "executionInfo": { "status": "ok", "timestamp": 1769839788714, "user_tz": -330, "elapsed": 1533, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "9914a1f7-0cde-4f1f-e9c8-fa459e17bb15" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🔍 INSPECTING FOLDER: /content/drive/MyDrive/LSTM_Wheat_Results/\n", "\n", "✅ Found 4 CSV files.\n", "\n", "FILENAME | SIZE (MB) | STATUS | COLUMNS CHECK\n", "--------------------------------------------------------------------------------------------------------------\n", "Wheat_S1_Batch_A.csv | 4.27 | ⚠️ SMALL | ✅ S1 Data Found\n", "Wheat_S1_Batch_B.csv | 4.27 | ⚠️ SMALL | ✅ S1 Data Found\n", "Wheat_S2_Batch_A.csv | 5.89 | ✅ OK | ✅ S2 Data Found\n", "Wheat_S2_Batch_B.csv | 5.89 | ✅ OK | ✅ S2 Data Found\n", "\n", "--------------------------------------------------------------------------------------------------------------\n", "GUIDE:\n", "1. Sizes: S1 files should be ~10-15 MB. S2 files should be ~40-60 MB.\n", "2. Columns: You MUST see '✅ Data Found'. If you see '❌ MISSING', the selectors failed.\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "# PART 2: THE REFINEMENT GATE\n", "import pandas as pd\n", "import numpy as np\n", "from scipy.interpolate import interp1d\n", "from scipy.signal import savgol_filter\n", "from tqdm.notebook import tqdm\n", "import os\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "COMMON_START = pd.Timestamp('2023-10-01')\n", "TARGET_DATES = [COMMON_START + pd.Timedelta(days=d) for d in range(213) if (COMMON_START + pd.Timedelta(days=d)).day in [1, 15]]\n", "TARGET_DAYS_INT = [(t - COMMON_START).days for t in TARGET_DATES]\n", "\n", "def remove_spikes(series):\n", " s = series.copy()\n", " diff = s.diff().abs()\n", " # Now that data is normalized (0-1), 0.4 is a valid threshold\n", " s.loc[diff > 0.4] = np.nan\n", " return s\n", "\n", "print(\"1. Loading Data & Normalizing...\")\n", "try:\n", " s1_a = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_A.csv'); s1_a['batch'] = 'A'\n", " s2_a = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_A.csv'); s2_a['batch'] = 'A'\n", " s1_b = pd.read_csv(INPUT_DIR + 'Wheat_S1_Batch_B.csv'); s1_b['batch'] = 'B'\n", " s2_b = pd.read_csv(INPUT_DIR + 'Wheat_S2_Batch_B.csv'); s2_b['batch'] = 'B'\n", "\n", " df = pd.concat([s1_a, s2_a, s1_b, s2_b], ignore_index=True)\n", "\n", " # 1. Clean 0s (Nodata)\n", " sensor_cols = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']\n", " cols_exist = [c for c in sensor_cols if c in df.columns]\n", " df[cols_exist] = df[cols_exist].replace(0, np.nan)\n", "\n", " # 2. THE FIX: NORMALIZE OPTICAL DATA (0-10000 -> 0.0-1.0)\n", " # This makes the values compatible with the Spike Filter (0.4)\n", " optical_cols = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']\n", " opt_exist = [c for c in optical_cols if c in df.columns]\n", " df[opt_exist] = df[opt_exist] / 10000.0\n", "\n", " # 3. ID Override (Keep merging logic)\n", " df['id_body'] = df['system:index'].apply(lambda x: '_'.join(str(x).split('_')[1:]))\n", " df['unique_id'] = df['batch'] + '_' + df['id_body']\n", "\n", " # 4. Class Fix\n", " df['class'] = df['class'].fillna(-1).astype(int)\n", "\n", " df['date'] = pd.to_datetime(df['date'])\n", "\n", " # 5. Radar Linear Conversion\n", " for b in ['VV', 'VH']:\n", " mask = (df['sensor_type'] == 'S1') & (df[b].notna())\n", " df.loc[mask, b] = 10**(df.loc[mask, b]/10.0)\n", "\n", " print(\" Data Loaded & Normalized correctly.\")\n", "\n", "except Exception as e:\n", " raise Exception(f\" Setup Failed: {e}\")\n", "\n", "X_list, y_list = [], []\n", "grouped = df.groupby('unique_id')\n", "\n", "print(f\"2. Processing {len(grouped)} Unique Farms...\")\n", "\n", "for pid, group in tqdm(grouped, total=len(grouped)):\n", "\n", " try:\n", " label_val = group['class'].max()\n", " if label_val == -1: continue\n", " except: continue\n", "\n", " valid_s2 = group[group['sensor_type'] == 'S2'].dropna(subset=['B2'])\n", " if len(valid_s2) < 3:\n", " continue\n", "\n", " p_mat, valid = [], True\n", " bands = ['VV', 'VH', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']\n", "\n", " for band in bands:\n", " ts = group[['date', band]].dropna().groupby('date').mean().sort_index()\n", " ts_vals = ts[band]\n", " ts_dates = ts.index\n", "\n", " # Spike Filter (Now safe because data is 0-1)\n", " if band.startswith('B'):\n", " ts_vals = remove_spikes(ts_vals)\n", " mask = ~np.isnan(ts_vals)\n", " ts_vals = ts_vals[mask]; ts_dates = ts_dates[mask]\n", "\n", " if len(ts_vals) < 2:\n", " valid = False; break\n", "\n", " try:\n", " f = interp1d((ts_dates - COMMON_START).days, ts_vals, kind='linear', fill_value='extrapolate')\n", " res = f(TARGET_DAYS_INT)\n", " except:\n", " valid = False; break\n", "\n", " try:\n", " window = 5 if len(res) >= 5 else 3\n", " smoothed = savgol_filter(res, window, 2)\n", " except:\n", " smoothed = res\n", "\n", " p_mat.append(smoothed)\n", "\n", " if valid:\n", " X_list.append(np.array(p_mat).T)\n", " y_list.append(label_val)\n", "\n", "X_data = np.array(X_list)\n", "y_data = np.array(y_list)\n", "\n", "if len(X_data) > 0:\n", " np.save(INPUT_DIR + 'X_wheat.npy', X_data)\n", " np.save(INPUT_DIR + 'y_wheat.npy', y_data)\n", " print(f\"\\n SUCCESS: Dataset saved. Shape: {X_data.shape}\")\n", "else:\n", " print(\"\\n FAILURE: Still 0 points. Logic mismatch persists.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 136, "referenced_widgets": [ "fa0ade09bf8e4069b38b9368f68a5756", "39389c81f0fd4f8a93cc5b281244f85e", "0db0d811611343da8dbe8774b54e7f9d", "383e80043cc34548886ccaa54d17001a", "7b0c5d2df00646a5b69241f3cbd8b217", "db89b3d03d744ab2b59585eb9f9b5167", "13c87a0a79174d0b87e226730d45178a", "fa4e6eecee8440a48f0c4048c0efaadc", "26de19876be64010aa3d07617ed664e6", "2b5e171407994a3eb5c7c4e993e8ae65", "1b08420a6cbc46bea2dbc73ed6fe532d" ] }, "id": "TsphmhKoh4F4", "executionInfo": { "status": "ok", "timestamp": 1769851966356, "user_tz": -330, "elapsed": 486728, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "353d7dcd-e3b2-4519-b9a5-e395aaf675cc" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1. Loading Data & Normalizing...\n", " Data Loaded & Normalized correctly.\n", "2. Processing 10000 Unique Farms...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/10000 [00:00" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# ==========================================\n", "# PART 4: DATA HEALTH CHECK (POST-PROCESSING)\n", "# ==========================================\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "print(\"--- DATA QUALITY ASSURANCE REPORT ---\")\n", "\n", "try:\n", " # 1. Load the Saved Data\n", " X = np.load(INPUT_DIR + 'X_wheat.npy')\n", " y = np.load(INPUT_DIR + 'y_wheat.npy')\n", " print(f\" Files Loaded.\")\n", " print(f\" X Shape: {X.shape} (Farms, Dates, Features)\")\n", " print(f\" y Shape: {y.shape} (Labels)\")\n", "\n", " # 2. Check for \"Silent Killers\" (NaNs or Infinite values)\n", " nan_count = np.isnan(X).sum()\n", " inf_count = np.isinf(X).sum()\n", " if nan_count == 0 and inf_count == 0:\n", " print(\"CLEANLINESS CHECK PASSED: No NaNs or Infinite values found.\")\n", " else:\n", " print(f\" WARNING: Found {nan_count} NaNs and {inf_count} Infs!\")\n", "\n", " # 3. Value Range Check (Are the numbers physically realistic?)\n", " # We expect Optical to be 0.0 - 1.0 (Reflectance)\n", " # We expect Radar (Linear) to be roughly 0.0 - 0.5\n", " print(\"\\n[STATISTICAL CHECK]\")\n", " print(f\" Global Min Value: {X.min():.4f}\")\n", " print(f\" Global Max Value: {X.max():.4f}\")\n", " print(f\" Mean Value: {X.mean():.4f}\")\n", "\n", " if X.max() > 100:\n", " print(\" WARNING: Max value is huge (>100). Did Normalization fail?\")\n", " elif X.max() < 0.001:\n", " print(\" WARNING: Max value is tiny (<0.001). Signal might be lost.\")\n", " else:\n", " print(\" RANGE CHECK PASSED: Values look like normalized satellite data.\")\n", "\n", " # 4. Class Balance Check\n", " unique, counts = np.unique(y, return_counts=True)\n", " balance = dict(zip(unique, counts))\n", " print(\"\\n[CLASS BALANCE]\")\n", " print(f\" Labels found: {balance}\")\n", " if len(balance) < 2:\n", " print(\" CRITICAL FAILURE: Only one class exists! Model cannot learn.\")\n", " else:\n", " print(\" BALANCE CHECK PASSED: Both Wheat and Non-Wheat exist.\")\n", "\n", " # 5. Visual Sanity Check (The \"Eye Test\")\n", " # Plot a random Wheat farm vs a Non-Wheat farm\n", " print(\"\\n[VISUAL INSPECTION]\")\n", " print(\" Plotting random samples to check for 'Crop Curves'...\")\n", "\n", " wheat_indices = np.where(y == 1)[0]\n", " non_wheat_indices = np.where(y == 0)[0]\n", "\n", " if len(wheat_indices) > 0 and len(non_wheat_indices) > 0:\n", " # Pick random ones\n", " w_idx = np.random.choice(wheat_indices)\n", " n_idx = np.random.choice(non_wheat_indices)\n", "\n", " plt.figure(figsize=(14, 5))\n", "\n", " # Plot Wheat (Band 2 vs Band 8 - Red vs NIR usually shows growth)\n", " plt.subplot(1, 2, 1)\n", " plt.plot(X[w_idx, :, 2], label='B2 (Blue)', marker='o') # Index 2 is B2\n", " plt.plot(X[w_idx, :, 8], label='B8 (NIR)', marker='o', color='green') # Index 8 is B8\n", " plt.title(f'Sample WHEAT Farm (ID {w_idx})')\n", " plt.xlabel('Time Steps (Fortnights)')\n", " plt.ylabel('Reflectance (0-1)')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # Plot Non-Wheat\n", " plt.subplot(1, 2, 2)\n", " plt.plot(X[n_idx, :, 2], label='B2 (Blue)', marker='o')\n", " plt.plot(X[n_idx, :, 8], label='B8 (NIR)', marker='o', color='green')\n", " plt.title(f'Sample NON-WHEAT Farm (ID {n_idx})')\n", " plt.xlabel('Time Steps (Fortnights)')\n", " plt.ylabel('Reflectance (0-1)')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " plt.show()\n", " print(\" (Look at the plots: Wheat should show a 'Green Bump' in B8. Non-Wheat is often flat or random.)\")\n", "\n", "except Exception as e:\n", " print(f\" Error during QA: {e}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 817 }, "id": "TRL7FueMhrnS", "executionInfo": { "status": "ok", "timestamp": 1769854129673, "user_tz": -330, "elapsed": 2347, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "9cadca76-4782-4ead-e80e-e266baaff435" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--- DATA QUALITY ASSURANCE REPORT ---\n", "✅ Files Loaded.\n", " X Shape: (10000, 14, 12) (Farms, Dates, Features)\n", " y Shape: (10000,) (Labels)\n", "✅ CLEANLINESS CHECK PASSED: No NaNs or Infinite values found.\n", "\n", "[STATISTICAL CHECK]\n", " Global Min Value: -0.6523\n", " Global Max Value: 30.3852\n", " Mean Value: 0.1692\n", "✅ RANGE CHECK PASSED: Values look like normalized satellite data.\n", "\n", "[CLASS BALANCE]\n", " Labels found: {np.int64(0): np.int64(5000), np.int64(1): np.int64(5000)}\n", "✅ BALANCE CHECK PASSED: Both Wheat and Non-Wheat exist.\n", "\n", "[VISUAL INSPECTION]\n", " Plotting random samples to check for 'Crop Curves'...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " (Look at the plots: Wheat should show a 'Green Bump' in B8. Non-Wheat is often flat or random.)\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "# PART 3: THE INTELLIGENCE GATE\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", "from sklearn.model_selection import train_test_split\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "\n", "# 1. Force CPU Mode\n", "tf.config.set_visible_devices([], 'GPU')\n", "print(\" CPU MODE ACTIVE. Using Smart Callbacks to save time.\")\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "print(\"1. Loading Processed Data...\")\n", "try:\n", " X = np.load(INPUT_DIR + 'X_wheat.npy')\n", " y = np.load(INPUT_DIR + 'y_wheat.npy')\n", " print(f\" Data Loaded. Shape: {X.shape}\")\n", "except FileNotFoundError:\n", " raise Exception(\" Data not found.\")\n", "\n", "# 2. Split Data\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "# 3. Build the Researcher-Grade LSTM (Same robust architecture)\n", "model = Sequential([\n", " LSTM(64, return_sequences=True, input_shape=(14, 12)),\n", " BatchNormalization(),\n", " Dropout(0.3),\n", " LSTM(32, return_sequences=False),\n", " BatchNormalization(),\n", " Dropout(0.3),\n", " Dense(16, activation='relu'),\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "model.compile(optimizer=Adam(learning_rate=0.001),\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])\n", "\n", "# --- THE SMART TRAINING TOOLS ---\n", "callbacks = [\n", " # 1. Early Stopping: Stop if no improvement for 25 epochs\n", " EarlyStopping(monitor='val_loss', patience=25, verbose=1, restore_best_weights=True),\n", "\n", " # 2. Model Checkpoint: ALWAYS save the best model found so far\n", " ModelCheckpoint(\n", " filepath=INPUT_DIR + 'Best_Wheat_LSTM.h5',\n", " monitor='val_loss',\n", " save_best_only=True, # Only overwrite if this epoch is better\n", " verbose=1\n", " )\n", "]\n", "\n", "print(\"\\n2. Starting Smart Training...\")\n", "print(\" - Max Epochs: 200\")\n", "print(\" - Stop if no progress for: 25 epochs\")\n", "print(\" - Auto-Saving best model to Drive\")\n", "\n", "start_time = time.time()\n", "\n", "history = model.fit(\n", " X_train, y_train,\n", " epochs=200, # Cap at 200 (1000 is too risky on CPU)\n", " batch_size=32,\n", " validation_data=(X_test, y_test),\n", " callbacks=callbacks, # Activate the tools\n", " verbose=1\n", ")\n", "\n", "end_time = time.time()\n", "duration = (end_time - start_time) / 60\n", "print(f\"\\n TRAINING COMPLETE in {duration:.1f} minutes.\")\n", "print(f\" Best Model saved as: {INPUT_DIR}Best_Wheat_LSTM.h5\")\n", "\n", "# 4. Visualization\n", "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['accuracy'], label='Train Accuracy')\n", "plt.plot(history.history['val_accuracy'], label='Test Accuracy')\n", "plt.title('Accuracy Curve')\n", "plt.legend(); plt.grid(True)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='Train Loss')\n", "plt.plot(history.history['val_loss'], label='Test Loss')\n", "plt.title('Loss Curve')\n", "plt.legend(); plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "wautujLulFn1", "executionInfo": { "status": "ok", "timestamp": 1769855168614, "user_tz": -330, "elapsed": 132826, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "b46c83ac-90d1-42ba-c57b-378710c04d36" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " CPU MODE ACTIVE. Using Smart Callbacks to save time.\n", "1. Loading Processed Data...\n", " Data Loaded. Shape: (10000, 14, 12)\n", "\n", "2. Starting Smart Training...\n", " - Max Epochs: 200\n", " - Stop if no progress for: 25 epochs\n", " - Auto-Saving best model to Drive\n", "Epoch 1/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.5077 - loss: 0.7456\n", "Epoch 1: val_loss improved from inf to 0.69432, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM.h5\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 26ms/step - accuracy: 0.5077 - loss: 0.7455 - val_accuracy: 0.4850 - val_loss: 0.6943\n", "Epoch 2/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.5107 - loss: 0.7033\n", "Epoch 2: val_loss did not improve from 0.69432\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.5107 - loss: 0.7033 - val_accuracy: 0.5260 - val_loss: 0.6945\n", "Epoch 3/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.4993 - loss: 0.7028\n", "Epoch 3: val_loss did not improve from 0.69432\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.4994 - loss: 0.7028 - val_accuracy: 0.4855 - val_loss: 0.7000\n", "Epoch 4/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5040 - loss: 0.6972\n", "Epoch 4: val_loss improved from 0.69432 to 0.69287, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM.h5\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - accuracy: 0.5040 - loss: 0.6972 - val_accuracy: 0.5150 - val_loss: 0.6929\n", "Epoch 5/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.5045 - loss: 0.6980\n", "Epoch 5: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - accuracy: 0.5045 - loss: 0.6980 - val_accuracy: 0.4960 - val_loss: 0.6942\n", "Epoch 6/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.4952 - loss: 0.6969\n", "Epoch 6: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.4952 - loss: 0.6969 - val_accuracy: 0.5115 - val_loss: 0.6943\n", "Epoch 7/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.4919 - loss: 0.6969\n", "Epoch 7: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.4919 - loss: 0.6969 - val_accuracy: 0.4835 - val_loss: 0.6945\n", "Epoch 8/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5055 - loss: 0.6969\n", "Epoch 8: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - accuracy: 0.5055 - loss: 0.6969 - val_accuracy: 0.5195 - val_loss: 0.6934\n", "Epoch 9/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.5174 - loss: 0.6957\n", "Epoch 9: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.5173 - loss: 0.6957 - val_accuracy: 0.4885 - val_loss: 0.6952\n", "Epoch 10/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5088 - loss: 0.6946\n", "Epoch 10: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5086 - loss: 0.6946 - val_accuracy: 0.4895 - val_loss: 0.6946\n", "Epoch 11/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5046 - loss: 0.6952\n", "Epoch 11: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5046 - loss: 0.6952 - val_accuracy: 0.4995 - val_loss: 0.6944\n", "Epoch 12/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5018 - loss: 0.6960\n", "Epoch 12: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 18ms/step - accuracy: 0.5018 - loss: 0.6960 - val_accuracy: 0.4945 - val_loss: 0.6931\n", "Epoch 13/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5000 - loss: 0.6957\n", "Epoch 13: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.4999 - loss: 0.6957 - val_accuracy: 0.4940 - val_loss: 0.6933\n", "Epoch 14/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5001 - loss: 0.6943\n", "Epoch 14: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5001 - loss: 0.6944 - val_accuracy: 0.5040 - val_loss: 0.6944\n", "Epoch 15/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.5181 - loss: 0.6924\n", "Epoch 15: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5180 - loss: 0.6924 - val_accuracy: 0.4965 - val_loss: 0.6930\n", "Epoch 16/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5093 - loss: 0.6940\n", "Epoch 16: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - accuracy: 0.5093 - loss: 0.6940 - val_accuracy: 0.5045 - val_loss: 0.6942\n", "Epoch 17/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.4952 - loss: 0.6937\n", "Epoch 17: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.4952 - loss: 0.6937 - val_accuracy: 0.4975 - val_loss: 0.6941\n", "Epoch 18/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.4948 - loss: 0.6960\n", "Epoch 18: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - accuracy: 0.4950 - loss: 0.6960 - val_accuracy: 0.5010 - val_loss: 0.6944\n", "Epoch 19/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.4913 - loss: 0.6949\n", "Epoch 19: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4914 - loss: 0.6949 - val_accuracy: 0.4980 - val_loss: 0.6940\n", "Epoch 20/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5112 - loss: 0.6936\n", "Epoch 20: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5110 - loss: 0.6936 - val_accuracy: 0.5025 - val_loss: 0.6930\n", "Epoch 21/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.4973 - loss: 0.6936\n", "Epoch 21: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.4973 - loss: 0.6936 - val_accuracy: 0.5160 - val_loss: 0.6930\n", "Epoch 22/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5070 - loss: 0.6926\n", "Epoch 22: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 20ms/step - accuracy: 0.5069 - loss: 0.6926 - val_accuracy: 0.4990 - val_loss: 0.6940\n", "Epoch 23/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5014 - loss: 0.6933\n", "Epoch 23: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5013 - loss: 0.6933 - val_accuracy: 0.4970 - val_loss: 0.6931\n", "Epoch 24/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5088 - loss: 0.6935\n", "Epoch 24: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.5087 - loss: 0.6935 - val_accuracy: 0.4925 - val_loss: 0.6936\n", "Epoch 25/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.5089 - loss: 0.6931\n", "Epoch 25: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5089 - loss: 0.6931 - val_accuracy: 0.4975 - val_loss: 0.6942\n", "Epoch 26/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.4964 - loss: 0.6950\n", "Epoch 26: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.4964 - loss: 0.6950 - val_accuracy: 0.4975 - val_loss: 0.6944\n", "Epoch 27/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5150 - loss: 0.6932\n", "Epoch 27: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5150 - loss: 0.6932 - val_accuracy: 0.4900 - val_loss: 0.6954\n", "Epoch 28/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.4949 - loss: 0.6944\n", "Epoch 28: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - accuracy: 0.4949 - loss: 0.6944 - val_accuracy: 0.5020 - val_loss: 0.6948\n", "Epoch 29/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.4867 - loss: 0.6950\n", "Epoch 29: val_loss did not improve from 0.69287\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.4868 - loss: 0.6950 - val_accuracy: 0.5025 - val_loss: 0.6948\n", "Epoch 29: early stopping\n", "Restoring model weights from the end of the best epoch: 4.\n", "\n", " TRAINING COMPLETE in 2.2 minutes.\n", " Best Model saved as: /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM.h5\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "\n", "# PART 3: THE INTELLIGENCE GATE\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "\n", "# 1. Force CPU Mode\n", "tf.config.set_visible_devices([], 'GPU')\n", "print(\" CPU MODE ACTIVE. Training with StandardScaler.\")\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "print(\"1. Loading Data...\")\n", "try:\n", " X = np.load(INPUT_DIR + 'X_wheat.npy')\n", " y = np.load(INPUT_DIR + 'y_wheat.npy')\n", " print(f\" Data Loaded. Shape: {X.shape}\")\n", "except FileNotFoundError:\n", " raise Exception(\"Data not found.\")\n", "\n", "# --- THE FIX: STANDARD SCALING ---\n", "# We must reshape to 2D to scale, then reshape back to 3D for LSTM\n", "print(\"2. Normalizing Features (StandardScaler)...\")\n", "N, T, F = X.shape # (Farms, Time, Features)\n", "\n", "# Flatten: (10000 farms * 14 dates, 12 features)\n", "X_flat = X.reshape(N * T, F)\n", "\n", "# Scale: Make all bands have Mean=0, Std=1\n", "scaler = StandardScaler()\n", "X_scaled_flat = scaler.fit_transform(X_flat)\n", "\n", "# Reshape back: (10000, 14, 12)\n", "X_scaled = X_scaled_flat.reshape(N, T, F)\n", "print(f\" Scaling complete. Radar and Optical are now balanced.\")\n", "# ---------------------------------\n", "\n", "# 3. Split Data\n", "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n", "\n", "# 4. Build Model (Same Architecture)\n", "model = Sequential([\n", " LSTM(64, return_sequences=True, input_shape=(14, 12)),\n", " BatchNormalization(),\n", " Dropout(0.3),\n", " LSTM(32, return_sequences=False),\n", " BatchNormalization(),\n", " Dropout(0.3),\n", " Dense(16, activation='relu'),\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "# Reduced Learning Rate slightly to prevent \"bouncing\"\n", "model.compile(optimizer=Adam(learning_rate=0.0005),\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])\n", "\n", "callbacks = [\n", " EarlyStopping(monitor='val_loss', patience=40, verbose=1, restore_best_weights=True),\n", " ModelCheckpoint(\n", " filepath=INPUT_DIR + 'Best_Wheat_LSTM_Scaled.h5',\n", " monitor='val_loss',\n", " save_best_only=True,\n", " verbose=1\n", " )\n", "]\n", "\n", "print(\"\\n3. Starting Training (Scaled)...\")\n", "start_time = time.time()\n", "\n", "history = model.fit(\n", " X_train, y_train,\n", " epochs=200, # 200 is plenty if data is scaled correctly\n", " batch_size=32,\n", " validation_data=(X_test, y_test),\n", " callbacks=callbacks,\n", " verbose=1\n", ")\n", "\n", "end_time = time.time()\n", "print(f\"\\n TRAINING COMPLETE in {(end_time - start_time)/60:.1f} minutes.\")\n", "\n", "# 5. Visualization\n", "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['accuracy'], label='Train Accuracy')\n", "plt.plot(history.history['val_accuracy'], label='Test Accuracy')\n", "plt.title('Accuracy Curve (Should go > 80%)')\n", "plt.legend(); plt.grid(True)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='Train Loss')\n", "plt.plot(history.history['val_loss'], label='Test Loss')\n", "plt.title('Loss Curve')\n", "plt.legend(); plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "1jlQJA8Qnxy5", "executionInfo": { "status": "ok", "timestamp": 1769855986659, "user_tz": -330, "elapsed": 247882, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "cc7a58e8-3ae8-4d5f-be9a-1f6c491680f6" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " CPU MODE ACTIVE. Training with StandardScaler.\n", "1. Loading Data...\n", " Data Loaded. Shape: (10000, 14, 12)\n", "2. Normalizing Features (StandardScaler)...\n", " Scaling complete. Radar and Optical are now balanced.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/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.\n", " super().__init__(**kwargs)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "3. Starting Training (Scaled)...\n", "Epoch 1/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.4972 - loss: 0.7782\n", "Epoch 1: val_loss improved from inf to 0.69851, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Scaled.h5\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 16ms/step - accuracy: 0.4972 - loss: 0.7780 - val_accuracy: 0.5015 - val_loss: 0.6985\n", "Epoch 2/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.4995 - loss: 0.7286\n", "Epoch 2: val_loss did not improve from 0.69851\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.4996 - loss: 0.7285 - val_accuracy: 0.5085 - val_loss: 0.6991\n", "Epoch 3/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.4967 - loss: 0.7143\n", "Epoch 3: val_loss did not improve from 0.69851\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - accuracy: 0.4968 - loss: 0.7143 - val_accuracy: 0.5040 - val_loss: 0.7036\n", "Epoch 4/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.5163 - loss: 0.7037\n", "Epoch 4: val_loss did not improve from 0.69851\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - accuracy: 0.5162 - loss: 0.7037 - val_accuracy: 0.4970 - val_loss: 0.7024\n", "Epoch 5/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5171 - loss: 0.7026\n", "Epoch 5: val_loss did not improve from 0.69851\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - accuracy: 0.5170 - loss: 0.7027 - val_accuracy: 0.4820 - val_loss: 0.6991\n", "Epoch 6/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.4932 - loss: 0.7063\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.4934 - loss: 0.7062 - val_accuracy: 0.5060 - val_loss: 0.6974\n", "Epoch 7/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5051 - loss: 0.6998\n", "Epoch 7: val_loss did not improve from 0.69743\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5052 - loss: 0.6998 - val_accuracy: 0.5050 - val_loss: 0.7005\n", "Epoch 8/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.5001 - loss: 0.7023\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 32ms/step - accuracy: 0.5001 - loss: 0.7023 - val_accuracy: 0.4915 - val_loss: 0.6953\n", "Epoch 9/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5018 - loss: 0.7017\n", "Epoch 9: val_loss did not improve from 0.69526\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 14ms/step - accuracy: 0.5019 - loss: 0.7017 - val_accuracy: 0.5110 - val_loss: 0.6960\n", "Epoch 10/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5181 - loss: 0.6950\n", "Epoch 10: val_loss did not improve from 0.69526\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.5181 - loss: 0.6950 - val_accuracy: 0.5035 - val_loss: 0.6991\n", "Epoch 11/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5170 - loss: 0.6959\n", "Epoch 11: val_loss did not improve from 0.69526\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5170 - loss: 0.6959 - val_accuracy: 0.4900 - val_loss: 0.6984\n", "Epoch 12/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5191 - loss: 0.6964\n", "Epoch 12: val_loss did not improve from 0.69526\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.5191 - loss: 0.6964 - val_accuracy: 0.4925 - val_loss: 0.7002\n", "Epoch 13/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5249 - loss: 0.6941\n", "Epoch 13: val_loss did not improve from 0.69526\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.5248 - loss: 0.6941 - val_accuracy: 0.5065 - val_loss: 0.6990\n", "Epoch 14/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5239 - loss: 0.6938\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - accuracy: 0.5239 - loss: 0.6938 - val_accuracy: 0.5010 - val_loss: 0.6952\n", "Epoch 15/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5229 - loss: 0.6939\n", "Epoch 15: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5229 - loss: 0.6939 - val_accuracy: 0.4875 - val_loss: 0.6980\n", "Epoch 16/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5241 - loss: 0.6933\n", "Epoch 16: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.5240 - loss: 0.6933 - val_accuracy: 0.4965 - val_loss: 0.7006\n", "Epoch 17/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5267 - loss: 0.6930\n", "Epoch 17: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5267 - loss: 0.6930 - val_accuracy: 0.4905 - val_loss: 0.6970\n", "Epoch 18/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5092 - loss: 0.6943\n", "Epoch 18: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.5092 - loss: 0.6943 - val_accuracy: 0.4840 - val_loss: 0.6993\n", "Epoch 19/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5270 - loss: 0.6915\n", "Epoch 19: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.5269 - loss: 0.6915 - val_accuracy: 0.4990 - val_loss: 0.6978\n", "Epoch 20/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5190 - loss: 0.6922\n", "Epoch 20: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5190 - loss: 0.6922 - val_accuracy: 0.4925 - val_loss: 0.6979\n", "Epoch 21/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5178 - loss: 0.6941\n", "Epoch 21: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.5178 - loss: 0.6941 - val_accuracy: 0.4845 - val_loss: 0.6996\n", "Epoch 22/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5202 - loss: 0.6937\n", "Epoch 22: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step - accuracy: 0.5202 - loss: 0.6936 - val_accuracy: 0.4900 - val_loss: 0.6980\n", "Epoch 23/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5272 - loss: 0.6903\n", "Epoch 23: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5272 - loss: 0.6903 - val_accuracy: 0.4820 - val_loss: 0.7021\n", "Epoch 24/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5220 - loss: 0.6935\n", "Epoch 24: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - accuracy: 0.5220 - loss: 0.6935 - val_accuracy: 0.4850 - val_loss: 0.6985\n", "Epoch 25/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5177 - loss: 0.6925\n", "Epoch 25: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 18ms/step - accuracy: 0.5178 - loss: 0.6925 - val_accuracy: 0.4860 - val_loss: 0.6982\n", "Epoch 26/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5167 - loss: 0.6937\n", "Epoch 26: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5168 - loss: 0.6937 - val_accuracy: 0.4820 - val_loss: 0.6995\n", "Epoch 27/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5192 - loss: 0.6930\n", "Epoch 27: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5191 - loss: 0.6931 - val_accuracy: 0.4725 - val_loss: 0.6977\n", "Epoch 28/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.5310 - loss: 0.6897\n", "Epoch 28: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.5310 - loss: 0.6897 - val_accuracy: 0.5065 - val_loss: 0.6965\n", "Epoch 29/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5286 - loss: 0.6916\n", "Epoch 29: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5285 - loss: 0.6916 - val_accuracy: 0.4850 - val_loss: 0.6995\n", "Epoch 30/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5190 - loss: 0.6926\n", "Epoch 30: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.5191 - loss: 0.6926 - val_accuracy: 0.4760 - val_loss: 0.6991\n", "Epoch 31/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5256 - loss: 0.6907\n", "Epoch 31: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.5257 - loss: 0.6907 - val_accuracy: 0.4875 - val_loss: 0.6990\n", "Epoch 32/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5297 - loss: 0.6909\n", "Epoch 32: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5297 - loss: 0.6909 - val_accuracy: 0.4905 - val_loss: 0.7030\n", "Epoch 33/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5430 - loss: 0.6878\n", "Epoch 33: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5429 - loss: 0.6879 - val_accuracy: 0.5110 - val_loss: 0.6979\n", "Epoch 34/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5359 - loss: 0.6887\n", "Epoch 34: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 20ms/step - accuracy: 0.5359 - loss: 0.6887 - val_accuracy: 0.5020 - val_loss: 0.7013\n", "Epoch 35/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5419 - loss: 0.6891\n", "Epoch 35: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5419 - loss: 0.6891 - val_accuracy: 0.4920 - val_loss: 0.7009\n", "Epoch 36/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5274 - loss: 0.6889\n", "Epoch 36: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5274 - loss: 0.6889 - val_accuracy: 0.4975 - val_loss: 0.7003\n", "Epoch 37/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5220 - loss: 0.6903\n", "Epoch 37: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - accuracy: 0.5220 - loss: 0.6903 - val_accuracy: 0.4890 - val_loss: 0.6987\n", "Epoch 38/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5277 - loss: 0.6910\n", "Epoch 38: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.5277 - loss: 0.6910 - val_accuracy: 0.5010 - val_loss: 0.6993\n", "Epoch 39/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5555 - loss: 0.6839\n", "Epoch 39: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5552 - loss: 0.6840 - val_accuracy: 0.4960 - val_loss: 0.7010\n", "Epoch 40/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5402 - loss: 0.6869\n", "Epoch 40: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5402 - loss: 0.6869 - val_accuracy: 0.4900 - val_loss: 0.7009\n", "Epoch 41/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5483 - loss: 0.6851\n", "Epoch 41: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.5482 - loss: 0.6851 - val_accuracy: 0.4785 - val_loss: 0.7025\n", "Epoch 42/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5478 - loss: 0.6840\n", "Epoch 42: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5478 - loss: 0.6840 - val_accuracy: 0.5010 - val_loss: 0.7015\n", "Epoch 43/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5385 - loss: 0.6860\n", "Epoch 43: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5385 - loss: 0.6860 - val_accuracy: 0.4785 - val_loss: 0.7054\n", "Epoch 44/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.5508 - loss: 0.6839\n", "Epoch 44: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - accuracy: 0.5507 - loss: 0.6840 - val_accuracy: 0.4975 - val_loss: 0.7024\n", "Epoch 45/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5471 - loss: 0.6870\n", "Epoch 45: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - accuracy: 0.5471 - loss: 0.6870 - val_accuracy: 0.5025 - val_loss: 0.7030\n", "Epoch 46/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5458 - loss: 0.6853\n", "Epoch 46: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.5458 - loss: 0.6853 - val_accuracy: 0.4865 - val_loss: 0.7055\n", "Epoch 47/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5553 - loss: 0.6822\n", "Epoch 47: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - accuracy: 0.5553 - loss: 0.6822 - val_accuracy: 0.5035 - val_loss: 0.7048\n", "Epoch 48/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5548 - loss: 0.6829\n", "Epoch 48: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - accuracy: 0.5548 - loss: 0.6829 - val_accuracy: 0.4995 - val_loss: 0.7032\n", "Epoch 49/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5543 - loss: 0.6814\n", "Epoch 49: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5543 - loss: 0.6815 - val_accuracy: 0.5105 - val_loss: 0.7031\n", "Epoch 50/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5559 - loss: 0.6802\n", "Epoch 50: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.5558 - loss: 0.6803 - val_accuracy: 0.5125 - val_loss: 0.7021\n", "Epoch 51/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.5651 - loss: 0.6795\n", "Epoch 51: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 19ms/step - accuracy: 0.5650 - loss: 0.6795 - val_accuracy: 0.5080 - val_loss: 0.7020\n", "Epoch 52/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5716 - loss: 0.6766\n", "Epoch 52: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.5714 - loss: 0.6767 - val_accuracy: 0.5080 - val_loss: 0.7032\n", "Epoch 53/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5501 - loss: 0.6826\n", "Epoch 53: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.5502 - loss: 0.6826 - val_accuracy: 0.4925 - val_loss: 0.7053\n", "Epoch 54/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5464 - loss: 0.6800\n", "Epoch 54: val_loss did not improve from 0.69519\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 18ms/step - accuracy: 0.5464 - loss: 0.6800 - val_accuracy: 0.5015 - val_loss: 0.7059\n", "Epoch 54: early stopping\n", "Restoring model weights from the end of the best epoch: 14.\n", "\n", " TRAINING COMPLETE in 4.1 minutes.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "\n", "# PART 3: THE INTELLIGENCE GATE (LIGHTWEIGHT MODEL)\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "\n", "# 1. Force CPU Mode\n", "tf.config.set_visible_devices([], 'GPU')\n", "print(\" CPU MODE ACTIVE. Training Lightweight Model.\")\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "# 2. Load & Scale Data (Re-running just to be safe)\n", "try:\n", " X = np.load(INPUT_DIR + 'X_wheat.npy')\n", " y = np.load(INPUT_DIR + 'y_wheat.npy')\n", "\n", " # Scale (Critical Step)\n", " N, T, F = X.shape\n", " X_flat = X.reshape(N * T, F)\n", " scaler = StandardScaler()\n", " X_scaled_flat = scaler.fit_transform(X_flat)\n", " X_scaled = X_scaled_flat.reshape(N, T, F)\n", "\n", " # Split\n", " X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n", " print(\" Data Loaded & Scaled.\")\n", "except:\n", " raise Exception(\" Data missing.\")\n", "\n", "# 3. The \"Lightweight\" Architecture\n", "# Single Layer LSTM + High Dropout = Forced Generalization\n", "model = Sequential([\n", " # Reduced from 64 to 32 units\n", " LSTM(32, return_sequences=False, input_shape=(14, 12)),\n", "\n", " # Batch Normalization keeps weights stable\n", " BatchNormalization(),\n", "\n", " # Increased Dropout (50% of neurons turned off randomly)\n", " # This forces the model to not rely on any single feature\n", " Dropout(0.5),\n", "\n", " # Simple Decision Layer\n", " Dense(16, activation='relu'),\n", " Dropout(0.2), # Extra dropout before final decision\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "model.compile(optimizer=Adam(learning_rate=0.0005), # Gentle learning rate\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])\n", "\n", "callbacks = [\n", " # Patience 40 is enough for a small model\n", " EarlyStopping(monitor='val_loss', patience=40, verbose=1, restore_best_weights=True),\n", " ModelCheckpoint(INPUT_DIR + 'Best_Wheat_LSTM_Light.h5', monitor='val_loss', save_best_only=True, verbose=1)\n", "]\n", "\n", "print(\"\\n3. Starting Training (Lightweight)...\")\n", "start_time = time.time()\n", "\n", "history = model.fit(\n", " X_train, y_train,\n", " epochs=200,\n", " batch_size=32,\n", " validation_data=(X_test, y_test),\n", " callbacks=callbacks,\n", " verbose=1\n", ")\n", "\n", "end_time = time.time()\n", "print(f\"\\n TRAINING COMPLETE in {(end_time - start_time)/60:.1f} minutes.\")\n", "\n", "# Visualization\n", "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['accuracy'], label='Train')\n", "plt.plot(history.history['val_accuracy'], label='Test')\n", "plt.title('Accuracy (Lightweight)')\n", "plt.legend(); plt.grid(True)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='Train')\n", "plt.plot(history.history['val_loss'], label='Test')\n", "plt.title('Loss (Lightweight)')\n", "plt.legend(); plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "x1adRXJupTZg", "executionInfo": { "status": "ok", "timestamp": 1769856277174, "user_tz": -330, "elapsed": 133416, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "cb66b467-aef4-4410-8312-efaf2e7ce97b" }, "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " CPU MODE ACTIVE. Training Lightweight Model.\n", " Data Loaded & Scaled.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/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.\n", " super().__init__(**kwargs)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "3. Starting Training (Lightweight)...\n", "Epoch 1/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5023 - loss: 0.8261\n", "Epoch 1: val_loss improved from inf to 0.69800, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Light.h5\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.5024 - loss: 0.8249 - val_accuracy: 0.4895 - val_loss: 0.6980\n", "Epoch 2/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5126 - loss: 0.7570\n", "Epoch 2: val_loss did not improve from 0.69800\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5125 - loss: 0.7569 - val_accuracy: 0.5035 - val_loss: 0.6986\n", "Epoch 3/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4978 - loss: 0.7320\n", "Epoch 3: val_loss did not improve from 0.69800\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.4978 - loss: 0.7319 - val_accuracy: 0.5010 - val_loss: 0.7001\n", "Epoch 4/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5090 - loss: 0.7098\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5089 - loss: 0.7099 - val_accuracy: 0.5110 - val_loss: 0.6969\n", "Epoch 5/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5104 - loss: 0.7076\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5101 - loss: 0.7077 - val_accuracy: 0.5125 - val_loss: 0.6936\n", "Epoch 6/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5013 - loss: 0.7043\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.5014 - loss: 0.7043 - val_accuracy: 0.5120 - val_loss: 0.6935\n", "Epoch 7/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5025 - loss: 0.7065\n", "Epoch 7: val_loss did not improve from 0.69346\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.5024 - loss: 0.7065 - val_accuracy: 0.5075 - val_loss: 0.6935\n", "Epoch 8/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5044 - loss: 0.7022\n", "Epoch 8: val_loss did not improve from 0.69346\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5043 - loss: 0.7022 - val_accuracy: 0.4955 - val_loss: 0.6938\n", "Epoch 9/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5046 - loss: 0.7000\n", "Epoch 9: val_loss did not improve from 0.69346\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5046 - loss: 0.7000 - val_accuracy: 0.4955 - val_loss: 0.6942\n", "Epoch 10/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5101 - loss: 0.6962\n", "Epoch 10: val_loss did not improve from 0.69346\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5099 - loss: 0.6962 - val_accuracy: 0.5015 - val_loss: 0.6936\n", "Epoch 11/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4968 - loss: 0.6994\n", "Epoch 11: val_loss did not improve from 0.69346\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.4970 - loss: 0.6993 - val_accuracy: 0.5045 - val_loss: 0.6936\n", "Epoch 12/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5041 - loss: 0.6974\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.5041 - loss: 0.6974 - val_accuracy: 0.5025 - val_loss: 0.6934\n", "Epoch 13/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5004 - loss: 0.6951\n", "Epoch 13: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5005 - loss: 0.6952 - val_accuracy: 0.4940 - val_loss: 0.6937\n", "Epoch 14/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.4984 - loss: 0.6953\n", "Epoch 14: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.4984 - loss: 0.6954 - val_accuracy: 0.4955 - val_loss: 0.6935\n", "Epoch 15/200\n", "\u001b[1m243/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5057 - loss: 0.6948\n", "Epoch 15: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5057 - loss: 0.6948 - val_accuracy: 0.4950 - val_loss: 0.6937\n", "Epoch 16/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5074 - loss: 0.6939\n", "Epoch 16: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5075 - loss: 0.6939 - val_accuracy: 0.4925 - val_loss: 0.6940\n", "Epoch 17/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5100 - loss: 0.6937\n", "Epoch 17: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5099 - loss: 0.6937 - val_accuracy: 0.4825 - val_loss: 0.6940\n", "Epoch 18/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5052 - loss: 0.6939\n", "Epoch 18: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5053 - loss: 0.6939 - val_accuracy: 0.4825 - val_loss: 0.6940\n", "Epoch 19/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5040 - loss: 0.6948\n", "Epoch 19: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5040 - loss: 0.6948 - val_accuracy: 0.4950 - val_loss: 0.6941\n", "Epoch 20/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5080 - loss: 0.6942\n", "Epoch 20: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5080 - loss: 0.6942 - val_accuracy: 0.4915 - val_loss: 0.6939\n", "Epoch 21/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5172 - loss: 0.6926\n", "Epoch 21: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.5172 - loss: 0.6926 - val_accuracy: 0.4890 - val_loss: 0.6942\n", "Epoch 22/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5121 - loss: 0.6941\n", "Epoch 22: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5122 - loss: 0.6941 - val_accuracy: 0.4850 - val_loss: 0.6941\n", "Epoch 23/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5071 - loss: 0.6928\n", "Epoch 23: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5073 - loss: 0.6928 - val_accuracy: 0.5005 - val_loss: 0.6935\n", "Epoch 24/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5042 - loss: 0.6929\n", "Epoch 24: val_loss did not improve from 0.69345\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5043 - loss: 0.6929 - val_accuracy: 0.4935 - val_loss: 0.6935\n", "Epoch 25/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5100 - loss: 0.6929\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5101 - loss: 0.6929 - val_accuracy: 0.4950 - val_loss: 0.6934\n", "Epoch 26/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5175 - loss: 0.6925\n", "Epoch 26: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5174 - loss: 0.6925 - val_accuracy: 0.4990 - val_loss: 0.6935\n", "Epoch 27/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5189 - loss: 0.6921\n", "Epoch 27: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.5187 - loss: 0.6921 - val_accuracy: 0.4885 - val_loss: 0.6936\n", "Epoch 28/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5064 - loss: 0.6926\n", "Epoch 28: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.5065 - loss: 0.6926 - val_accuracy: 0.4925 - val_loss: 0.6940\n", "Epoch 29/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5100 - loss: 0.6937\n", "Epoch 29: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5099 - loss: 0.6937 - val_accuracy: 0.4905 - val_loss: 0.6938\n", "Epoch 30/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5115 - loss: 0.6921\n", "Epoch 30: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5115 - loss: 0.6921 - val_accuracy: 0.5095 - val_loss: 0.6939\n", "Epoch 31/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5050 - loss: 0.6927\n", "Epoch 31: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.5050 - loss: 0.6927 - val_accuracy: 0.4955 - val_loss: 0.6945\n", "Epoch 32/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5124 - loss: 0.6931\n", "Epoch 32: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5125 - loss: 0.6931 - val_accuracy: 0.5035 - val_loss: 0.6937\n", "Epoch 33/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5042 - loss: 0.6921\n", "Epoch 33: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5044 - loss: 0.6921 - val_accuracy: 0.5115 - val_loss: 0.6936\n", "Epoch 34/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5149 - loss: 0.6924\n", "Epoch 34: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.5150 - loss: 0.6924 - val_accuracy: 0.5055 - val_loss: 0.6942\n", "Epoch 35/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5288 - loss: 0.6906\n", "Epoch 35: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.5287 - loss: 0.6906 - val_accuracy: 0.5085 - val_loss: 0.6940\n", "Epoch 36/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5129 - loss: 0.6919\n", "Epoch 36: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5130 - loss: 0.6919 - val_accuracy: 0.4860 - val_loss: 0.6956\n", "Epoch 37/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5188 - loss: 0.6911\n", "Epoch 37: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5189 - loss: 0.6911 - val_accuracy: 0.5050 - val_loss: 0.6937\n", "Epoch 38/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5223 - loss: 0.6901\n", "Epoch 38: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5223 - loss: 0.6901 - val_accuracy: 0.5115 - val_loss: 0.6941\n", "Epoch 39/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5041 - loss: 0.6930\n", "Epoch 39: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5043 - loss: 0.6930 - val_accuracy: 0.5165 - val_loss: 0.6942\n", "Epoch 40/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5257 - loss: 0.6921\n", "Epoch 40: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5256 - loss: 0.6921 - val_accuracy: 0.5070 - val_loss: 0.6945\n", "Epoch 41/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5291 - loss: 0.6904\n", "Epoch 41: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5289 - loss: 0.6904 - val_accuracy: 0.5085 - val_loss: 0.6946\n", "Epoch 42/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5320 - loss: 0.6904\n", "Epoch 42: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5318 - loss: 0.6904 - val_accuracy: 0.5080 - val_loss: 0.6939\n", "Epoch 43/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5228 - loss: 0.6920\n", "Epoch 43: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.5227 - loss: 0.6920 - val_accuracy: 0.5075 - val_loss: 0.6947\n", "Epoch 44/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5188 - loss: 0.6918\n", "Epoch 44: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5189 - loss: 0.6918 - val_accuracy: 0.5105 - val_loss: 0.6961\n", "Epoch 45/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5223 - loss: 0.6900\n", "Epoch 45: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5223 - loss: 0.6900 - val_accuracy: 0.5010 - val_loss: 0.6958\n", "Epoch 46/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5251 - loss: 0.6898\n", "Epoch 46: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5251 - loss: 0.6898 - val_accuracy: 0.5055 - val_loss: 0.6957\n", "Epoch 47/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5213 - loss: 0.6894\n", "Epoch 47: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.5214 - loss: 0.6894 - val_accuracy: 0.4970 - val_loss: 0.6962\n", "Epoch 48/200\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5331 - loss: 0.6900\n", "Epoch 48: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.5331 - loss: 0.6900 - val_accuracy: 0.5000 - val_loss: 0.6961\n", "Epoch 49/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5250 - loss: 0.6925\n", "Epoch 49: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5250 - loss: 0.6925 - val_accuracy: 0.5010 - val_loss: 0.6963\n", "Epoch 50/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5339 - loss: 0.6885\n", "Epoch 50: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5335 - loss: 0.6886 - val_accuracy: 0.5090 - val_loss: 0.6965\n", "Epoch 51/200\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5398 - loss: 0.6891\n", "Epoch 51: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5395 - loss: 0.6891 - val_accuracy: 0.5080 - val_loss: 0.6978\n", "Epoch 52/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5366 - loss: 0.6891\n", "Epoch 52: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5361 - loss: 0.6891 - val_accuracy: 0.5015 - val_loss: 0.6972\n", "Epoch 53/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5257 - loss: 0.6897\n", "Epoch 53: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5257 - loss: 0.6897 - val_accuracy: 0.5050 - val_loss: 0.6959\n", "Epoch 54/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5313 - loss: 0.6891\n", "Epoch 54: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.5312 - loss: 0.6890 - val_accuracy: 0.5020 - val_loss: 0.6976\n", "Epoch 55/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5365 - loss: 0.6903\n", "Epoch 55: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5365 - loss: 0.6903 - val_accuracy: 0.5015 - val_loss: 0.6974\n", "Epoch 56/200\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5247 - loss: 0.6883\n", "Epoch 56: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5248 - loss: 0.6883 - val_accuracy: 0.5070 - val_loss: 0.7016\n", "Epoch 57/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5195 - loss: 0.6912\n", "Epoch 57: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5196 - loss: 0.6912 - val_accuracy: 0.5005 - val_loss: 0.6966\n", "Epoch 58/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5397 - loss: 0.6887\n", "Epoch 58: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5395 - loss: 0.6887 - val_accuracy: 0.5125 - val_loss: 0.6967\n", "Epoch 59/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5346 - loss: 0.6884\n", "Epoch 59: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.5344 - loss: 0.6884 - val_accuracy: 0.5070 - val_loss: 0.6976\n", "Epoch 60/200\n", "\u001b[1m241/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5378 - loss: 0.6886\n", "Epoch 60: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5376 - loss: 0.6886 - val_accuracy: 0.5005 - val_loss: 0.6978\n", "Epoch 61/200\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5395 - loss: 0.6878\n", "Epoch 61: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5393 - loss: 0.6878 - val_accuracy: 0.4980 - val_loss: 0.6978\n", "Epoch 62/200\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5273 - loss: 0.6891\n", "Epoch 62: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5273 - loss: 0.6891 - val_accuracy: 0.4955 - val_loss: 0.6983\n", "Epoch 63/200\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5384 - loss: 0.6852\n", "Epoch 63: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5383 - loss: 0.6853 - val_accuracy: 0.5010 - val_loss: 0.6992\n", "Epoch 64/200\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5327 - loss: 0.6854\n", "Epoch 64: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5328 - loss: 0.6854 - val_accuracy: 0.4910 - val_loss: 0.6986\n", "Epoch 65/200\n", "\u001b[1m242/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5399 - loss: 0.6847\n", "Epoch 65: val_loss did not improve from 0.69343\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.5398 - loss: 0.6848 - val_accuracy: 0.5065 - val_loss: 0.6982\n", "Epoch 65: early stopping\n", "Restoring model weights from the end of the best epoch: 25.\n", "\n", " TRAINING COMPLETE in 2.2 minutes.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# ==========================================\n", "# PART 3: THE DIAGNOSTIC (OPTICAL ONLY)\n", "# ==========================================\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "import matplotlib.pyplot as plt\n", "import os\n", "\n", "# Force CPU\n", "tf.config.set_visible_devices([], 'GPU')\n", "print(\" DIAGNOSTIC MODE: Optical Data Only.\")\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "try:\n", " X = np.load(INPUT_DIR + 'X_wheat.npy')\n", " y = np.load(INPUT_DIR + 'y_wheat.npy')\n", "\n", " # --- CRITICAL CHANGE: DROP RADAR ---\n", " # Original Shape: (10000, 14, 12) -> [VV, VH, B2, B3...]\n", " # We want indices 2 through 11 (The 10 Optical Bands)\n", " X_optical = X[:, :, 2:]\n", " print(f\" Original Shape: {X.shape}\")\n", " print(f\" Optical Shape: {X_optical.shape} (Radar Removed)\")\n", "\n", " # Scale Optical Data\n", " N, T, F = X_optical.shape\n", " X_flat = X_optical.reshape(N * T, F)\n", " scaler = StandardScaler()\n", " X_scaled_flat = scaler.fit_transform(X_flat)\n", " X_scaled = X_scaled_flat.reshape(N, T, F)\n", "\n", " # Split\n", " X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n", "\n", "except:\n", " raise Exception(\" Data missing.\")\n", "\n", "# Model (Standard Architecture)\n", "model = Sequential([\n", " LSTM(64, return_sequences=False, input_shape=(14, 10)), # Input shape is now 10 features\n", " BatchNormalization(),\n", " Dropout(0.4),\n", " Dense(32, activation='relu'),\n", " Dropout(0.2),\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "model.compile(optimizer=Adam(learning_rate=0.0005),\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])\n", "\n", "callbacks = [\n", " EarlyStopping(monitor='val_loss', patience=30, verbose=1, restore_best_weights=True),\n", " ModelCheckpoint(INPUT_DIR + 'Best_Wheat_LSTM_Optical.h5', monitor='val_loss', save_best_only=True, verbose=1)\n", "]\n", "\n", "print(\"\\nStarting Optical-Only Training...\")\n", "history = model.fit(\n", " X_train, y_train,\n", " epochs=150,\n", " batch_size=32,\n", " validation_data=(X_test, y_test),\n", " callbacks=callbacks,\n", " verbose=1\n", ")\n", "\n", "# Plot\n", "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['accuracy'], label='Train')\n", "plt.plot(history.history['val_accuracy'], label='Test')\n", "plt.title('Accuracy (Optical Only)')\n", "plt.legend(); plt.grid(True)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='Train')\n", "plt.plot(history.history['val_loss'], label='Test')\n", "plt.title('Loss (Optical Only)')\n", "plt.legend(); plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "NHlxIn6Nqay9", "executionInfo": { "status": "ok", "timestamp": 1769856529855, "user_tz": -330, "elapsed": 106277, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "91be6233-0649-4966-c2ee-0be3e4870d40" }, "execution_count": 27, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " DIAGNOSTIC MODE: Optical Data Only.\n", " Original Shape: (10000, 14, 12)\n", " Optical Shape: (10000, 14, 10) (Radar Removed)\n", "\n", "Starting Optical-Only Training...\n", "Epoch 1/150\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/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.\n", " super().__init__(**kwargs)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5009 - loss: 0.8491\n", "Epoch 1: val_loss improved from inf to 0.69641, saving model to /content/drive/MyDrive/LSTM_Wheat_Results/Best_Wheat_LSTM_Optical.h5\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.5009 - loss: 0.8481 - val_accuracy: 0.4960 - val_loss: 0.6964\n", "Epoch 2/150\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.4998 - loss: 0.7548\n", "Epoch 2: val_loss did not improve from 0.69641\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.4998 - loss: 0.7548 - val_accuracy: 0.4950 - val_loss: 0.6997\n", "Epoch 3/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.4817 - loss: 0.7417\n", "Epoch 3: val_loss did not improve from 0.69641\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.4819 - loss: 0.7415 - val_accuracy: 0.4960 - val_loss: 0.7078\n", "Epoch 4/150\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5018 - loss: 0.7216\n", "Epoch 4: val_loss did not improve from 0.69641\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5017 - loss: 0.7216 - val_accuracy: 0.4935 - val_loss: 0.6975\n", "Epoch 5/150\n", "\u001b[1m243/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5121 - loss: 0.7129\n", "Epoch 5: val_loss did not improve from 0.69641\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5119 - loss: 0.7129 - val_accuracy: 0.4785 - val_loss: 0.6977\n", "Epoch 6/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5029 - loss: 0.7130\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5028 - loss: 0.7130 - val_accuracy: 0.4920 - val_loss: 0.6961\n", "Epoch 7/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5196 - loss: 0.7021\n", "Epoch 7: val_loss did not improve from 0.69614\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5195 - loss: 0.7021 - val_accuracy: 0.4845 - val_loss: 0.6981\n", "Epoch 8/150\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5086 - loss: 0.7038\n", "Epoch 8: val_loss did not improve from 0.69614\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5086 - loss: 0.7037 - val_accuracy: 0.5050 - val_loss: 0.6983\n", "Epoch 9/150\n", "\u001b[1m243/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.5078 - loss: 0.7011\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.5078 - loss: 0.7011 - val_accuracy: 0.4865 - val_loss: 0.6953\n", "Epoch 10/150\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5040 - loss: 0.6995\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5040 - loss: 0.6995 - val_accuracy: 0.5020 - val_loss: 0.6951\n", "Epoch 11/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5020 - loss: 0.7004\n", "Epoch 11: val_loss did not improve from 0.69514\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5019 - loss: 0.7004 - val_accuracy: 0.4900 - val_loss: 0.6952\n", "Epoch 12/150\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5029 - loss: 0.6972\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5028 - loss: 0.6972 - val_accuracy: 0.4995 - val_loss: 0.6942\n", "Epoch 13/150\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5073 - loss: 0.6976\n", "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\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "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')`. \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5072 - loss: 0.6976 - val_accuracy: 0.5090 - val_loss: 0.6932\n", "Epoch 14/150\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5002 - loss: 0.6974\n", "Epoch 14: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5002 - loss: 0.6974 - val_accuracy: 0.4855 - val_loss: 0.6943\n", "Epoch 15/150\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.5099 - loss: 0.6948\n", "Epoch 15: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.5099 - loss: 0.6948 - val_accuracy: 0.4965 - val_loss: 0.6932\n", "Epoch 16/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5140 - loss: 0.6951\n", "Epoch 16: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.5139 - loss: 0.6951 - val_accuracy: 0.5045 - val_loss: 0.6962\n", "Epoch 17/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.4858 - loss: 0.7001\n", "Epoch 17: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.4860 - loss: 0.7000 - val_accuracy: 0.5105 - val_loss: 0.6943\n", "Epoch 18/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5167 - loss: 0.6942\n", "Epoch 18: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5164 - loss: 0.6943 - val_accuracy: 0.4895 - val_loss: 0.6934\n", "Epoch 19/150\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5095 - loss: 0.6930\n", "Epoch 19: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.5094 - loss: 0.6930 - val_accuracy: 0.4975 - val_loss: 0.6937\n", "Epoch 20/150\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5106 - loss: 0.6936\n", "Epoch 20: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5104 - loss: 0.6936 - val_accuracy: 0.4950 - val_loss: 0.6945\n", "Epoch 21/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.5172 - loss: 0.6926\n", "Epoch 21: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.5172 - loss: 0.6926 - val_accuracy: 0.4840 - val_loss: 0.6947\n", "Epoch 22/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5125 - loss: 0.6933\n", "Epoch 22: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5125 - loss: 0.6933 - val_accuracy: 0.4895 - val_loss: 0.6952\n", "Epoch 23/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5068 - loss: 0.6934\n", "Epoch 23: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5069 - loss: 0.6934 - val_accuracy: 0.5030 - val_loss: 0.6936\n", "Epoch 24/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5050 - loss: 0.6940\n", "Epoch 24: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.5051 - loss: 0.6940 - val_accuracy: 0.4925 - val_loss: 0.6942\n", "Epoch 25/150\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5161 - loss: 0.6917\n", "Epoch 25: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5161 - loss: 0.6917 - val_accuracy: 0.4885 - val_loss: 0.6948\n", "Epoch 26/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5190 - loss: 0.6928\n", "Epoch 26: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.5189 - loss: 0.6928 - val_accuracy: 0.4890 - val_loss: 0.6935\n", "Epoch 27/150\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5096 - loss: 0.6939\n", "Epoch 27: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.5097 - loss: 0.6939 - val_accuracy: 0.4830 - val_loss: 0.6939\n", "Epoch 28/150\n", "\u001b[1m245/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5039 - loss: 0.6923\n", "Epoch 28: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5040 - loss: 0.6923 - val_accuracy: 0.4870 - val_loss: 0.6941\n", "Epoch 29/150\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5138 - loss: 0.6928\n", "Epoch 29: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5138 - loss: 0.6928 - val_accuracy: 0.5040 - val_loss: 0.6932\n", "Epoch 30/150\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5336 - loss: 0.6902\n", "Epoch 30: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.5331 - loss: 0.6902 - val_accuracy: 0.4760 - val_loss: 0.6941\n", "Epoch 31/150\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5186 - loss: 0.6929\n", "Epoch 31: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.5187 - loss: 0.6929 - val_accuracy: 0.4920 - val_loss: 0.6934\n", "Epoch 32/150\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5190 - loss: 0.6930\n", "Epoch 32: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.5190 - loss: 0.6930 - val_accuracy: 0.5045 - val_loss: 0.6935\n", "Epoch 33/150\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5188 - loss: 0.6910\n", "Epoch 33: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5185 - loss: 0.6911 - val_accuracy: 0.5045 - val_loss: 0.6932\n", "Epoch 34/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5090 - loss: 0.6932\n", "Epoch 34: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5091 - loss: 0.6932 - val_accuracy: 0.4930 - val_loss: 0.6947\n", "Epoch 35/150\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5163 - loss: 0.6917\n", "Epoch 35: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5164 - loss: 0.6917 - val_accuracy: 0.4990 - val_loss: 0.6934\n", "Epoch 36/150\n", "\u001b[1m244/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5213 - loss: 0.6913\n", "Epoch 36: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5212 - loss: 0.6913 - val_accuracy: 0.4960 - val_loss: 0.6938\n", "Epoch 37/150\n", "\u001b[1m247/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.5143 - loss: 0.6923\n", "Epoch 37: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.5143 - loss: 0.6923 - val_accuracy: 0.5015 - val_loss: 0.6935\n", "Epoch 38/150\n", "\u001b[1m243/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5197 - loss: 0.6913\n", "Epoch 38: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.5197 - loss: 0.6913 - val_accuracy: 0.4905 - val_loss: 0.6940\n", "Epoch 39/150\n", "\u001b[1m246/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5255 - loss: 0.6899\n", "Epoch 39: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5254 - loss: 0.6899 - val_accuracy: 0.4925 - val_loss: 0.6940\n", "Epoch 40/150\n", "\u001b[1m249/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5231 - loss: 0.6915\n", "Epoch 40: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5231 - loss: 0.6915 - val_accuracy: 0.4945 - val_loss: 0.6947\n", "Epoch 41/150\n", "\u001b[1m248/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5206 - loss: 0.6914\n", "Epoch 41: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5206 - loss: 0.6914 - val_accuracy: 0.5075 - val_loss: 0.6942\n", "Epoch 42/150\n", "\u001b[1m243/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5306 - loss: 0.6911\n", "Epoch 42: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.5306 - loss: 0.6911 - val_accuracy: 0.4955 - val_loss: 0.6941\n", "Epoch 43/150\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5353 - loss: 0.6885\n", "Epoch 43: val_loss did not improve from 0.69319\n", "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.5352 - loss: 0.6885 - val_accuracy: 0.4960 - val_loss: 0.6945\n", "Epoch 43: early stopping\n", "Restoring model weights from the end of the best epoch: 13.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# ==========================================\n", "# PART 4: THE ULTIMATE SEPARABILITY TEST\n", "# ==========================================\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "INPUT_DIR = '/content/drive/MyDrive/LSTM_Wheat_Results/'\n", "\n", "try:\n", " # 1. Load Data\n", " X = np.load(INPUT_DIR + 'X_wheat.npy')\n", " y = np.load(INPUT_DIR + 'y_wheat.npy')\n", " print(f\"Data Loaded: {X.shape}\")\n", "\n", " # 2. Separate Wheat and Non-Wheat\n", " wheat_indices = np.where(y == 1)[0]\n", " non_wheat_indices = np.where(y == 0)[0]\n", "\n", " X_wheat = X[wheat_indices]\n", " X_non_wheat = X[non_wheat_indices]\n", "\n", " # 3. Calculate the AVERAGE Farm for each class\n", " # We take the mean across all farms (axis 0)\n", " # Shape becomes (14, 12) -> Average value for each date/band\n", " avg_wheat = np.mean(X_wheat, axis=0)\n", " avg_non_wheat = np.mean(X_non_wheat, axis=0)\n", "\n", " # 4. Plot the comparison for Key Bands\n", " # Band 2 (Blue - Index 2) and Band 8 (NIR - Index 8) are critical\n", " # Radar VV (Index 0) is critical\n", "\n", " plt.figure(figsize=(18, 5))\n", "\n", " # --- PLOT 1: OPTICAL (NIR - Band 8) ---\n", " plt.subplot(1, 3, 1)\n", " plt.plot(avg_wheat[:, 8], label='Wheat (Avg)', color='green', linewidth=3)\n", " plt.plot(avg_non_wheat[:, 8], label='Non-Wheat (Avg)', color='orange', linewidth=3, linestyle='--')\n", " plt.title(\"AVERAGE Optical Growth (NIR - B8)\")\n", " plt.xlabel(\"Time (Fortnights)\")\n", " plt.ylabel(\"Reflectance\")\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # --- PLOT 2: RADAR (VV - Index 0) ---\n", " plt.subplot(1, 3, 2)\n", " plt.plot(avg_wheat[:, 0], label='Wheat (Avg)', color='blue', linewidth=3)\n", " plt.plot(avg_non_wheat[:, 0], label='Non-Wheat (Avg)', color='red', linewidth=3, linestyle='--')\n", " plt.title(\"AVERAGE Radar Signal (VV)\")\n", " plt.xlabel(\"Time (Fortnights)\")\n", " plt.ylabel(\"Backscatter (Linear)\")\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # --- PLOT 3: OPTICAL (Red Edge - B5 - Index 5) ---\n", " plt.subplot(1, 3, 3)\n", " plt.plot(avg_wheat[:, 5], label='Wheat (Avg)', color='purple', linewidth=3)\n", " plt.plot(avg_non_wheat[:, 5], label='Non-Wheat (Avg)', color='brown', linewidth=3, linestyle='--')\n", " plt.title(\"AVERAGE Red Edge (B5)\")\n", " plt.xlabel(\"Time (Fortnights)\")\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " plt.show()\n", "\n", "except Exception as e:\n", " print(f\"Error: {e}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 524 }, "id": "yivju-_JsR5W", "executionInfo": { "status": "ok", "timestamp": 1769856907614, "user_tz": -330, "elapsed": 3779, "user": { "displayName": "Kasimali Agharia", "userId": "15509882304159194540" } }, "outputId": "084849bb-2378-4b11-efc8-fc33a9e5abb7" }, "execution_count": 28, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Data Loaded: (10000, 14, 12)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "References\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "[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.\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​\n", "\n", "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.\n", "​" ], "metadata": { "id": "cJ0tyoifdVqa" } }, { "cell_type": "code", "source": [ "OLMOEARTH" ], "metadata": { "id": "OI7IJXWpdca_" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "why bilstm is better than lstm https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0299350" ], "metadata": { "id": "1-EOmiDDdjwo" } }, { "cell_type": "markdown", "source": [ "Bi-LSTM: 94–96% accuracy for crop classification (vs. RF 88–92%), particularly effective with abundant labeled data and GPU resources\n", "\n", "TWDTW: 88–92% accuracy, exceptionally robust with limited training samples (<50 fields), more interpretable, and computationally efficient" ], "metadata": { "id": "Q8_7zJLLdrl2" } }, { "cell_type": "markdown", "source": [ "Bi-LSTM: 94–96% accuracy for crop classification (vs. RF 88–92%), particularly effective with abundant labeled data and GPU resources\n", "\n", "TWDTW: 88–92% accuracy, exceptionally robust with limited training samples (<50 fields), more interpretable, and computationally efficient" ], "metadata": { "id": "EzPK9WOeeEjW" } }, { "cell_type": "markdown", "source": [ "| Study | Dataset | Bi-LSTM Accuracy | RF/SVM Baseline | Improvement |\n", "| --------------------------------------------- | ------------------------------------------------------ | ---------------- | ------------------------------- | ----------- |\n", "| Khan et al. (2024)plos+1 | Sentinel-2 + PlanetScope (smallholder farms, Pakistan) | 94–96% | 87–90% (RF, SVM, k-NN) | +4–9 pp |\n", "| Bandar et al. (2024)dergipark​ | Sentinel-2 BreizhCrop | >93% | 89–92% (Vanilla LSTM, CNN, SVM) | +1–4 pp |\n", "| Sentinel-1 SAR study (2024)mdpi​ | Sentinel-1 rice detection | Higher | Lower (traditional ML) | Significant |\n", "| Rußwurm et al. (2020, CVPR)openaccess.thecvf​ | Sentinel-2 temporal sequence | 92–94% | 88–90% (CNN, SVM) | +2–4 pp |\n", "| Early-season Canada (2024)ieeexplore.ieee​ | RCM + Sentinel-1/2 (June–July) | 85% (early) | 82% (XGBoost) | +3 pp |" ], "metadata": { "id": "aod-Du0ceJe8" } }, { "cell_type": "markdown", "source": [ "Why Bi-LSTM Outperforms LSTM (93–95% vs. 94–96%)\n", "Standard LSTM only processes sequences forward (Oct → Dec). It misses crucial information:\n", "\n", "Growth phase: \"NDVI rising → likely tillering → wheat (not rice)\"\n", "\n", "Senescence phase: \"NDVI falling after peak → mature wheat (not growing maize)\"\n", "\n", "Bi-LSTM adds the backward view:\n", "\n", "Grain filling phase happens after peak NDVI\n", "\n", "A pixel with falling NDVI in late Dec could be wheat heading/early grain fill OR senescent rice\n", "\n", "Backward LSTM differentiates: \"Dec 1 looks mature, Dec 15 is declining → mature phase pattern → wheat\"\n", "\n", "Empirical gain: 1–3 percentage points (94–96% Bi-LSTM vs. 93–95% LSTM)." ], "metadata": { "id": "pd-wZxSXeRPg" } }, { "cell_type": "markdown", "source": [ "| Factor | Bi-LSTM | TWDTW | Random Forest (Mohite) | Best for Wheat |\n", "| --------------------- | ------------------------- | ------------------ | ---------------------- | -------------------------- |\n", "| Accuracy | 90–94% | 88–92% | 88.31% | Bi-LSTM (if data abundant) |\n", "| Small samples (<50) | 88–91% | 92% | 75% | TWDTW |\n", "| Computation | 800 GPU hrs | 50 CPU min | 10 min | TWDTW |\n", "| Interpretability | Low (black-box) | High (patterns) | Medium | TWDTW |\n", "| Phenology extraction | Hard | Built-in | Manual thresholding | TWDTW |\n", "| Temporal gap handling | Excellent (bidirectional) | Good | Poor (cloud-sensitive) | Bi-LSTM |\n", "| Transferability | Retraining needed | Pattern adjustment | Retraining needed | TWDTW |\n", "| Publication novelty | High (2025) | Medium (2019–2023) | Low (2019) | Bi-LSTM |\n", "| Deployment timeline | Weeks to months | Days | Days | TWDTW |" ], "metadata": { "id": "8rccDTnSeXx2" } }, { "cell_type": "markdown", "source": [ "| Method | Expected Accuracy | Implementation Time | Compute Cost | Interpretability | Best For |\n", "| ----------------------------------- | ----------------- | ------------------- | ---------------- | ---------------- | ---------------------------------- |\n", "| Random Forest (Mohite et al., 2019) | 88.31% | 1 day | 10 min | Medium | Baseline |\n", "| Bi-LSTM (new approach) | 90–94% | 2–4 weeks | 800 GPU hrs | Low (black-box) | High accuracy, research novelty |\n", "| TWDTW (new approach) | 88–92% | 3–5 days | 50 CPU min | High | Rapid deployment, interpretability |\n", "| Bi-LSTM + TWDTW Ensemble | 92–95% | 1 month | 800 GPU + 50 CPU | Medium | Maximum accuracy, robustness |" ], "metadata": { "id": "dRM5-LJKek_I" } }, { "cell_type": "markdown", "source": [ "Definition\n", "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." ], "metadata": { "id": "H3GEZvf2eqpV" } }, { "cell_type": "markdown", "source": [ "Definition\n", "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.\n", "\n", "How Object-based TWDTW Works\n", "For wheat mapping using object-based TWDTW:\n", "\n", "Step 1: Image Segmentation\n", "\n", "Divide image into meaningful objects that match agricultural fields\n", "\n", "Objects typically contain 100–160 pixels (representing 1–2.5 hectares)\n", "\n", "Segmentation ensures objects are spectrally and spatially homogeneous" ], "metadata": { "id": "lHWuSNYFewOu" } }, { "cell_type": "markdown", "source": [ "\n", "\n", "​\n", "\n", "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.\n", "​\n", "\n", "\n" ], "metadata": { "id": "Qcs2CBAtf4yS" } }, { "cell_type": "markdown", "source": [ 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)" ], "metadata": { "id": "sXKTVEd6e0Lr" } } ] }