monsoon-crop-predictor 0.1.0

Advanced ML-based crop yield prediction system using monsoon and rainfall data

Monsoon Crop Predictor

An advanced machine learning-based crop yield prediction system that leverages monsoon patterns, rainfall data, and agricultural parameters to provide accurate crop yield forecasts for Rice, Wheat, and Maize.

🌾 Features

• Multi-Crop Prediction: Support for Rice, Wheat, and Maize yield prediction
• Advanced ML Models: Ensemble models combining LightGBM, XGBoost, Random Forest, and other algorithms
• Monsoon Pattern Analysis: Comprehensive analysis of rainfall patterns and trends
• Risk Assessment: Agricultural risk evaluation with mitigation recommendations
• Crop Recommendations: Optimal crop selection based on weather conditions
• RESTful API: Production-ready FastAPI backend with comprehensive endpoints
• Command Line Interface: Easy-to-use CLI for predictions and analysis
• Data Validation: Robust input validation and quality assessment
• Confidence Intervals: Prediction uncertainty quantification

📦 Installation

Basic Installation

pip install monsoon-crop-predictor

Installation with Optional Features

# With API support
pip install monsoon-crop-predictor[api]

# With CLI support
pip install monsoon-crop-predictor[cli]

# With visualization support
pip install monsoon-crop-predictor[visualization]

# With all optional features
pip install monsoon-crop-predictor[all]

Development Installation

git clone https://github.com/SubratDash67/Monsoon-Crop-Yield.git
cd monsoon-crop-predictor
pip install -e .[dev]

🚀 Quick Start

Python API

from monsoon_crop_predictor import CropYieldPredictor

# Initialize predictor
predictor = CropYieldPredictor()

# Make a prediction
input_data = {
    'Annual': 1200,        # Annual rainfall in mm
    'Jun-Sep': 800,        # Monsoon rainfall in mm
    'Year': 2024,          # Year
    'State Name': 'Punjab'  # Optional location
}

result = predictor.predict_single(
    input_data=input_data,
    crop='RICE',
    include_confidence=True
)

print(f"Predicted yield: {result['predicted_yield']:.2f} kg/ha")
print(f"Confidence: {result['confidence_score']:.2f}")

Command Line Interface

# Single prediction
crop-predictor predict --crop RICE --annual 1200 --monsoon 800 --year 2024 --confidence

# Batch predictions from file
crop-predictor predict --crop WHEAT --rainfall-data data.csv --output results.json

# Monsoon pattern analysis
crop-predictor analyze --type comprehensive --location "Maharashtra" --start-year 2010 --end-year 2023

# Crop recommendations
crop-predictor recommend --annual 1000 --monsoon 600 --year 2024 --location "Karnataka"

# Data validation
crop-predictor validate --data-file dataset.csv --crop RICE --output validation_report.json

REST API

Start the API server:

# Install with API support
pip install monsoon-crop-predictor[api]

# Start server
uvicorn monsoon_crop_predictor.api.endpoints:app --host 0.0.0.0 --port 8000

Make predictions via HTTP:

curl -X POST "http://localhost:8000/predict_yield" \
     -H "Content-Type: application/json" \
     -d '{
       "crop": "RICE",
       "Annual": 1200,
       "Jun-Sep": 800,
       "year": 2024,
       "include_confidence": true
     }'

📊 API Endpoints

Core Prediction Endpoints

• POST /predict_yield - Single crop yield prediction
• POST /batch_predict - Batch predictions for multiple inputs
• POST /crop_recommendations - Get optimal crop recommendations
• POST /risk_assessment - Assess agricultural risk factors

Analysis Endpoints

• POST /monsoon_analysis - Analyze monsoon patterns and trends
• GET /historical_data - Retrieve historical rainfall/yield data

Utility Endpoints

• GET /health - API health check
• GET /docs - Interactive API documentation
• GET /redoc - Alternative API documentation

🔧 Configuration

Environment Variables

# API Configuration
CROP_PREDICTOR_HOST=0.0.0.0
CROP_PREDICTOR_PORT=8000
CROP_PREDICTOR_WORKERS=4

# Model Configuration
CROP_PREDICTOR_MODEL_DIR=/path/to/models
CROP_PREDICTOR_LOG_LEVEL=INFO

Custom Configuration

from monsoon_crop_predictor.utils.config import Config

# Load custom configuration
config = Config.load_custom_config('config.json')

# Initialize with custom model directory
predictor = CropYieldPredictor(model_dir='/custom/model/path')

📈 Model Performance

Our ensemble models achieve strong performance across different crops:

Crop	R² Score
Rice	0.85
Wheat	0.82
Maize	0.88

🗂️ Data Requirements

Input Data Format

The system expects rainfall data with the following columns:

Year,Annual,Jan-Feb,Mar-May,Jun-Sep,Oct-Dec,State Name,Dist Name
2020,1200,45,120,800,235,Punjab,Ludhiana
2021,980,30,95,650,205,Punjab,Ludhiana

Required Fields

• Year: Year (2000-2030)
• Annual: Annual rainfall in mm
• Jun-Sep: Monsoon rainfall in mm (required for all crops)

Optional Fields

• Jan-Feb: Winter rainfall in mm
• Mar-May: Summer rainfall in mm
• Oct-Dec: Post-monsoon rainfall in mm
• State Name: State/region name
• Dist Name: District name

🔬 Advanced Features

Feature Engineering

The system automatically creates advanced features:

from monsoon_crop_predictor.core.feature_engineer import FeatureEngineer

engineer = FeatureEngineer()

# Create rainfall features
data_with_features = engineer.create_rainfall_features(data)

# Create temporal features
data_with_temporal = engineer.create_temporal_features(data_with_features)

# Complete feature engineering pipeline
engineered_data = engineer.engineer_features_pipeline(
    data=raw_data,
    target_col='RICE YIELD (Kg per ha)',
    create_interactions=True,
    feature_selection=True,
    n_features=25
)

Model Ensemble

from monsoon_crop_predictor.models.ensemble import EnsemblePredictor

ensemble = EnsemblePredictor()

# Create voting ensemble
voting_model = ensemble.create_voting_ensemble(models, X_train, y_train)

# Create stacking ensemble
stacking_model = ensemble.create_stacking_ensemble(models, X_train, y_train)

# Optimize ensemble weights
optimized_weights = ensemble.optimize_ensemble_weights(
    models, X_val, y_val, method='gradient_descent'
)

Data Validation

from monsoon_crop_predictor.core.validator import DataValidator

validator = DataValidator()

# Generate comprehensive validation report
report = validator.generate_validation_report(data, crop='RICE')

print(f"Data quality score: {report['quality_validation']['quality_score']:.2f}")
print(f"Validation passed: {report['overall_assessment']['overall_valid']}")

🧪 Testing

Run the test suite:

# Install development dependencies
pip install -e .[dev]

# Run tests
pytest

# Run tests with coverage
pytest --cov=monsoon_crop_predictor --cov-report=html

# Run specific test categories
pytest -m "not slow"  # Skip slow tests
pytest -m "unit"      # Run only unit tests

📚 Documentation

Generate Documentation

# Install documentation dependencies
pip install sphinx sphinx-rtd-theme

# Build documentation
cd docs
make html

Example Notebooks

Check the examples/ directory for Jupyter notebooks demonstrating:

• Basic usage and predictions
• Advanced feature engineering
• Model training and evaluation
• API usage examples
• Data analysis workflows

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone repository
git clone https://github.com/SubratDash67/Monsoon-Crop-Yield.git
cd monsoon-crop-predictor

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .[dev]

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Indian Meteorological Department (IMD) for rainfall data
• ICRISAT for crop yield datasets
• Scikit-learn and XGBoost communities for ML frameworks
• FastAPI team for the excellent web framework

📞 Support

• Documentation: https://monsoon-crop-predictor.readthedocs.io/
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🗺️ Roadmap

• Support for additional crops (Cotton, Sugarcane, etc.)
• Integration with satellite imagery data
• Real-time weather API integration
• Mobile app development
• Multi-language support
• Advanced visualization dashboard
• Model interpretability features
• Integration with IoT sensors

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Made with ❤️ for sustainable agriculture