Performance assessment of linear regression for crop yield prediction in comparison with contemporary models

Abstract

Accurate crop yield prediction requires both precision and interpretability for practical agricultural decision-making. This study is the first to systematically benchmark linear regression (LR) against gradient boosting baselines (XGBoost, LightGBM) alongside random forest (RF), support vector machine (SVM), and artificial neural network (ANN) for Indian agricultural yield forecasting, demonstrating that interpretable models can match or outperform black-box approaches. Using a comprehensive Indian agricultural dataset spanning multiple crops, states, and growing seasons (2000–2022), comprising 58,000 records across 22 states and 35 crop varieties, we analyzed features including cultivated area, rainfall, fertilizer applications, and pesticide usage. LR achieved R2 of 0.401 0.02 across 10-fold cross-validation, MSE of 480,239, MAE of 139.50, and RMSE of 692.99, outperforming complex models while maintaining full transparency. Transparent models allow policymakers to understand the impact of rainfall and fertilizer use, enabling evidence-based resource allocation. Results demonstrate that model complexity does not guarantee superior agricultural predictions, and LR provides interpretable coefficients—such as embedding LR coefficients into advisory tools to guide fertilizer recommendations and irrigation scheduling-enabling actionable agronomic insights for sustainable farming practices.