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.
Farooqui et al. (Mon,) studied this question.