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README

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+# Yield Prediction Model - Open Source Demo
+
+## Overview
+
+This project demonstrates machine learning models to predict vines yield at harvest time using remote sensing data, weather information, soil properties, and agronomic attributes.
+
+The models predict:
+- **TCH (Tons of Grapes per Hectare)**: Vines yield at harvest
+
+## Files
+
+- **`training_features_anonymized.csv`** - Dataset for model training (926 rows × 589 columns)
+- **`BuildModels_open_source.ipynb`** - Self-contained notebook for training prediction models
+
+## BuildModels_open_source.ipynb - Quick Start Guide
+
+### What It Does
+This notebook trains machine learning models to predict vines yield (TCH) at harvest time.
+
+### Input
+- **File**: `training_features_anonymized.csv` (926 harvest observations)
+- **Features**: Satellite data, weather, soil properties, and crop characteristics
+
+### Process
+1. **Load and prepare data**
+   - Read CSV file
+   - Encode categorical variables (variety, rootstock type)
+
+2. **Train models using Leave-One-Season-Out Cross-Validation**
+   - For each season: train on all other seasons, test on held-out season
+   - Algorithm: LightGBM with 31 leaves, 100 trees
+   - Remove outliers: TCH (0.1-60 tons/ha)
+
+3. **Evaluate performance**
+   - Calculate metrics: RMSE, MAE, R², MAPE
+   - Generate scatter plots and feature importance charts
+
+4. **Save final models**
+   - Train on complete dataset
+   - Export as `.joblib` files for future use
+
+### Output Files
+- `tch_model.joblib` - Yield prediction model
+- `tch_encoders.joblib` - Label encoders for categorical variables
+
+### Feature Set Used
+**Weather + Soil + Extra**:
+- 5 satellite spectral indices (NDVI, EVI, VARI, NDRE, NDWI) × 42 time steps = 210 features
+- Weather time series: precipitation, temperature, degree days (28 features)
+- Soil properties: clay, sand, nitrogen, at 4 depths (25 features)
+- Agronomic: variety, age, cut cycle, day of year (4 features)
+- Extra: rootstock type, spacing, coordinates (5 features)
+- **Total**: ~272 features
+
+
+## Requirements
+
+```
+numpy>=1.21.0
+pandas>=1.3.0
+scikit-learn>=1.0.0
+lightgbm>=3.3.0
+matplotlib>=3.4.0
+seaborn>=0.11.0
+jupyter>=1.0.0
+joblib>=1.0.0
+```
+
+## Usage
+
+### Installation
+
+```bash
+# Install dependencies
+pip install numpy pandas scikit-learn lightgbm matplotlib seaborn jupyter joblib
+```
+
+### Running the Notebook
+
+```bash
+# Navigate to the notebook directory
+cd open_source_model/
+
+# Launch Jupyter
+jupyter notebook
+
+# Open BuildModels_open_source.ipynb and run all cells
+```
+
+### Using the Trained Models
+
+```python
+import joblib
+import pandas as pd
+
+# Load models and encoders
+tch_model = joblib.load('tch_model.joblib')
+tch_encoders = joblib.load('tch_encoders.joblib')
+
+# Prepare your data (must have the same features)
+# X = pd.DataFrame(...)  # Your feature data
+
+# Make predictions
+tch_predictions = tch_model.predict(X)
+```

tch_encoders.joblib

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tch_model.joblib

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