Smart Farming Assistant

The Smart Farming Assistant is a machine learning-based system designed to aid farmers and agricultural planners

in making informed decisions about crop yield and market pricing. The system utilizes advanced algorithms such as

XGBoost and Random Forest to predict agricultural outcomes based on soil health, weather patterns, and historical

market data.

To ensure transparency and trust in the model’s predictions, the project incorporates SHAP (SHapley Additive

exPlanations) values, allowing users to interpret the influence of each input feature on the model’s output. This enhances

the explainability of the system, making it not only a powerful forecasting tool but also an educational aid for

understanding the relationships between environmental factors and crop performance.

The project includes a user-friendly web interface that enables users to input relevant agricultural parameters and

receive both predictions and interpretive visualizations. By combining accuracy with explainability, this Smart Farming

Assistant bridges the gap between traditional agricultural knowledge and modern artificial intelligence, promoting more

efficient and profitable farming practices.