An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction.

Agriculture is a major contributor to India's GDP and employs a large population. Key crops like rice are essential for food security, making higher yields crucial for sustainability. The use of machine learning (ML) in crop yield prediction has significantly improved forecast accuracy. However, the adoption of these models by policymakers and farmers is hindered by their lack of interpretability. Explainable Artificial Intelligence (XAI) techniques address this challenge by making AI-driven predictions more transparent, ensuring trust and better decision-making. This research integrates XAI techniques into a hybrid model that combines the powers of Random Forest (RF), Long Short-Term Memory (LSTM), and XGBoost algorithms by incorporating SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and Counterfactual Analysis for yield prediction. On a large-scale, multi-year agricultural dataset comprising over 246,000 records across 33 states, spanning crops, seasons, and climatic factors provided by the Indian Agriculture Department, the model achieved high accuracy (R² = 0.9827 for crop yield and 0.9721 for rice yield) outperforming existing models. The method involves:•Implementing a hybrid AI model to improve accuracy in yield predictions.•Integrating XAI methods to enhance model transparency and interpret nuanced feature interactions•Delivering actionable insights via the developed 'E-Kisan' web interface.