SHAP-enhanced ensemble learning for yield prediction: insights from CY-Bench data on Indian wheat

Abstract

Predicting crop yield accurately is essential for providing food security and improving agricultural practices. This study examines the use of ensemble machine learning models combined with Shapley additive explanations (SHAP) feature selection to improve wheat yield prediction in India. The study uses CY-Bench data, incorporating normalized difference vegetation index (NDVI), meteorological data, and soil moisture data for yield prediction. Various ensemble techniques, including voting, stacking, and boosting are evaluated. Boost m1 ensemble model consistently outperforms other models in the prediction. Additionally, the integration of SHAP-based feature selection with the best ensemble model significantly improves the model accuracy and interpretability by identifying the most influential features affecting yield. The results show the effectiveness of ensemble boosting model, in capturing the complex relationships within agricultural data particularly when combined with feature selection. This method improves the transparency and actionability of machine learning models for agronomists, policymakers, and farmers.