144 lines
5.1 KiB
JSON
144 lines
5.1 KiB
JSON
{
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"name": "307_dropout02_with_doy",
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"description": "Production Model 307: LSTM-based harvest detection (Phase 3, minimal regularization)",
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"model_info": {
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"type": "LSTM",
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"architecture": "Unidirectional LSTM with dual output heads (imminent + detected)",
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"total_parameters": 105120,
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"input_features": 14,
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"hidden_units": 256,
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"output_heads": 2,
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"training_data": "Historical multi-season CI data from multiple estates",
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"validation_method": "5-fold cross-validation",
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"device": "GPU (CUDA) or CPU fallback"
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},
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"production_scripts": {
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"baseline": {
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"script": "01_harvest_baseline_prediction.py",
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"frequency": "Run ONCE during setup",
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"purpose": "Predict all harvest dates (ground truth baseline)",
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"input": "ci_data_for_python.csv (complete historical data)",
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"output": "harvest_production_export.xlsx",
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"time_estimate": "5-30 minutes depending on data volume"
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},
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"monitoring": {
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"script": "02_harvest_imminent_weekly.py",
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"frequency": "Run WEEKLY (or daily if required)",
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"purpose": "Real-time harvest status and imminent alerts",
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"input": "ci_data_for_python.csv (recent data)",
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"output": "harvest_imminent_weekly.csv",
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"time_estimate": "1-5 minutes"
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}
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},
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"features": [
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"CI_raw",
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"7d_MA",
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"14d_MA",
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"21d_MA",
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"7d_velocity",
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"14d_velocity",
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"21d_velocity",
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"7d_min",
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"14d_min",
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"21d_min",
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"7d_std",
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"14d_std",
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"21d_std",
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"DOY_normalized"
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],
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"model": {
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"type": "LSTM",
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"hidden_size": 256,
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"num_layers": 1,
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"dropout": 0.2
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},
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"training": {
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"imminent_days_before": 28,
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"imminent_days_before_end": 1,
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"detected_days_after_start": 1,
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"detected_days_after_end": 21,
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"k_folds": 5,
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"num_epochs": 150,
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"patience": 20,
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"learning_rate": 0.001,
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"batch_size": 4
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},
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"data": {
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"csv_path": "../lstm_complete_data.csv",
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"ci_column": "FitData",
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"test_fraction": 0.15,
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"seed": 42
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},
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"workflow_instructions": {
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"overview": "Model 307 uses a two-script approach: baseline setup + weekly monitoring",
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"step_1_baseline": {
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"description": "Establish historical harvest date reference for all fields",
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"script": "01_harvest_baseline_prediction.py",
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"when": "Run once after setting up CI extraction pipeline",
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"command": "conda activate python_gpu && python 01_harvest_baseline_prediction.py",
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"input_data": "ci_data_for_python.csv (all available historical CI data)",
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"output_file": "harvest_production_export.xlsx (ground truth baseline)",
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"columns": [
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"field - Field ID",
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"sub_field - Sub-field designation",
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"season - Season number (1, 2, 3...)",
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"year - Year of harvest",
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"season_start_date - Start of growing season",
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"season_end_date - End of season (harvest date)",
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"phase1_harvest_date - Refined harvest prediction"
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],
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"notes": "This becomes your reference - compare all weekly monitoring against this"
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},
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"step_2_monitoring": {
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"description": "Weekly real-time harvest status and imminent alerts",
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"script": "02_harvest_imminent_weekly.py",
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"when": "Run every week (e.g., Mondays) or daily if near harvest",
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"command": "conda activate python_gpu && python 02_harvest_imminent_weekly.py",
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"input_data": "ci_data_for_python.csv (latest CI data from 02b conversion)",
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"output_file": "harvest_imminent_weekly.csv",
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"columns": [
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"field - Field ID",
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"sub_field - Sub-field designation",
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"imminent_prob - Likelihood of harvest readiness in next 28 days (0.0-1.0)",
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"detected_prob - Current harvest probability (0.0-1.0)",
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"week - ISO week number",
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"year - Year",
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"as_of_date - Latest date in dataset",
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"num_days - Days of history used"
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],
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"alert_thresholds": {
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"imminent_high": "imminent_prob > 0.7 (prepare harvest)",
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"imminent_medium": "imminent_prob 0.5-0.7 (monitor closely)",
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"detected_high": "detected_prob > 0.6 (active harvesting)"
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}
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},
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"integration_with_r_pipeline": {
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"before_model_307": [
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"Planet 8-band download: download_8band_pu_optimized.ipynb",
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"CI extraction: 02_ci_extraction.R",
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"Convert to CSV: 02b_convert_rds_to_csv.R (outputs ci_data_for_python.csv)"
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],
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"model_307_here": [
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"BASELINE: 01_harvest_baseline_prediction.py (run once)",
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"MONITORING: 02_harvest_imminent_weekly.py (run weekly)"
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],
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"after_model_307": [
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"Field analysis: 09b_field_analysis_weekly.R (reads harvest predictions)",
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"Reports: 10_CI_report_with_kpis.Rmd (includes harvest status)"
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]
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},
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"environment_requirements": {
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"python_env": "python_gpu",
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"activation": "conda activate python_gpu",
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"required_packages": [
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"torch (GPU-enabled)",
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"pandas",
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"numpy",
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"scikit-learn",
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"pyyaml",
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"openpyxl"
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],
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"gpu": "NVIDIA GPU with CUDA (optional - falls back to CPU if unavailable)"
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}
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}
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} |