This project predicts crop yields using ML models trained on historical agricultural data, weather, and soil health.
It also provides optimization recommendations for irrigation, fertilization, and pest control to help farmers improve productivity.
- Crop yield prediction based on multiple inputs
- Historical data comparison
- Optimization and actionable recommendations
- Scalable design for web/mobile integration
- Python, Pandas, NumPy, Scikit-learn
- Matplotlib, Seaborn for visualization
- APIs for weather & soil data
- Optional: Streamlit/Flask for user interface
git clone <repo-url>
cd crop-yield-ml
pip install -r requirements.txt