🌾 Crop Yield Prediction AI

A comprehensive machine learning system for predicting agricultural crop yields using advanced AI and geospatial data analysis.

🚀 Features

• Predictive Analytics: Forecast crop yields based on historical data and environmental factors
• Multi-Model Approach: Ensemble of machine learning models for accurate predictions
• Geospatial Integration: Incorporates satellite imagery and location-based features
• Real-time Monitoring: Live data processing and visualization
• RESTful API: Fully documented API for integration with other systems
• Interactive Dashboard: User-friendly Streamlit interface for data exploration

🛠 Tech Stack

• Backend: FastAPI, Python 3.9+
• Frontend: Streamlit
• ML Framework: Scikit-learn, XGBoost, LightGBM
• Geospatial: GeoPandas, Rasterio, Sentinel Hub
• Data Processing: Pandas, NumPy, Dask
• Visualization: Plotly, Matplotlib, Seaborn
• Validation: Pydantic
• Testing: Pytest, Hypothesis

📦 Installation

Prerequisites

• Python 3.9 or higher
• Poetry (dependency management)
• Git

Quick Start

1. Clone the repository

git clone <repository-url>
cd crop-yield-prediction-ai

2. Install dependencies using Poetry

poetry install

3. Download required NLP model

poetry run python -m spacy download en_core_web_sm

4. Environment Configuration

Create a .env file in the root directory:

# API Keys and External Services
SENTINEL_HUB_API_KEY=your_sentinel_hub_key
WEATHER_API_KEY=your_weather_api_key
DATABASE_URL=your_database_connection_string

# Model Configuration
MODEL_CACHE_DIR=./models
DATA_CACHE_DIR=./data

# Application Settings
DEBUG=False
LOG_LEVEL=INFO

🎯 Usage

Running the Web Interface

poetry run streamlit run app.py

Access the dashboard at: http://localhost:8501

Starting the API Server

poetry run uvicorn main_api:app --reload --host 0.0.0.0 --port 8000

API documentation available at: http://localhost:8000/docs

Production Deployment

# Start with production settings
poetry run uvicorn main_api:app --host 0.0.0.0 --port 8000 --workers 4

🧪 Testing

Run the complete test suite:

poetry run pytest

Specific test categories:

# Unit tests only
poetry run pytest tests/unit/

# Integration tests
poetry run pytest tests/integration/

# With coverage report
poetry run pytest --cov=src --cov-report=html

# Performance testing
poetry run pytest tests/performance/ -v

📁 Project Structure

crop-yield-prediction-ai/
├── src/                    # Source code
│   ├── models/            # ML model implementations
│   ├── data/              # Data processing modules
│   ├── api/               # API routes and handlers
│   └── utils/             # Utility functions
├── tests/                 # Test suites
├── data/                  # Data storage
│   ├── raw/              # Raw datasets
│   ├── processed/         # Processed data
│   └── models/           # Trained model files
├── notebooks/            # Jupyter notebooks for exploration
├── docs/                 # Documentation
└── config/               # Configuration files

🔧 Configuration

Model Parameters

Edit config/model_config.yaml to adjust:

• Feature engineering parameters
• Model hyperparameters
• Training configurations
• Validation settings

API Settings

Modify config/api_config.yaml for:

• Rate limiting
• CORS settings
• Authentication
• Cache configurations

📊 Data Sources

The system integrates multiple data sources:

• Satellite Imagery: Sentinel-2, Landsat 8
• Weather Data: Historical and forecast data
• Soil Data: Composition and quality metrics
• Agricultural Records: Historical yield data
• Economic Factors: Market prices and trends

🔍 API Endpoints

Core Endpoints

• POST /api/v1/predict - Generate yield predictions
• GET /api/v1/models - List available models
• POST /api/v1/train - Retrain models with new data
• GET /api/v1/health - System health check

Data Endpoints

• GET /api/v1/data/sources - Available data sources
• POST /api/v1/data/upload - Upload new datasets
• GET /api/v1/data/statistics - Dataset statistics

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

Development Setup

# Install development dependencies
poetry install --with dev

# Pre-commit hooks
poetry run pre-commit install

# Run linter
poetry run black src/ tests/
poetry run isort src/ tests/

📈 Performance Monitoring

The system includes:

• Real-time performance metrics
• Model drift detection
• Automated retraining pipelines
• Comprehensive logging

🚨 Troubleshooting

Common Issues

Missing Dependencies

poetry lock --no-update
poetry install

API Connection Issues

• Verify .env file configuration
• Check API key validity
• Ensure required ports are available

Model Loading Errors

• Clear cache: rm -rf ./models/cache
• Retrain models using the training endpoint

📄 License

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

🙏 Acknowledgments

• Satellite data providers: ESA Sentinel Hub, NASA Landsat
• Weather data sources: OpenWeatherMap, NOAA
• Agricultural research institutions and open data initiatives

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Maintainers: [Your Team/Organization]
Support: [Support Contact/Channel]
Version: 1.0.0