A fuzzy graph-based prediction model for crop yield forecasting under climatic variability

Accurate crop yield prediction under climatic variability is fundamental to food security planning and agricultural policy. Classical statistical and machine learning approaches often struggle to handle imprecise, incomplete, or linguistically described agronomic data arising from uncertain climatic conditions. In this paper, a novel fuzzy graph-based prediction model (FGBPM) that integrates fuzzy set theory with graph-theoretic structures is proposed to model complex, nonlinear relationships between climatic parameters and crop yield outcomes. Fuzzy membership functions are constructed for temperature, rainfall, humidity, and solar radiation. A weighted fuzzy relational graph is constructed over historical agroclimate datasets (2005–2024) from five agroecological zones in India. Fuzzy inference rules, derived via expert elicitation and data-driven correlation, propagate climatic uncertainty through the graph to generate probabilistic yield distributions. The FGBPM achieves a coefficient of determination (R²) of 0.961, a root mean square error (RMSE) of 0.112 t/ha, and a mean absolute error (MAE) of 0.089 t/ha on the test set, outperforming the artificial neural network (ANN), support vector machine (SVM), random forest, and linear regression baselines. An ablation study confirms that each model component—graph-based propagation, expert rule base, and α-cut uncertainty generation—independently contributes to overall performance, with graph propagation alone accounting for a 14.2% reduction in the RMSE relative to a stand-alone fuzzy inference system. The model generates quantified uncertainty intervals for seasonal yield forecasts, demonstrated across wheat, rice, maize, and soybean crops. Compared with existing methods, the proposed FGBPM demonstrates superior predictive accuracy, interpretability, and uncertainty quantification capacity. Open-source implementation supports reproducibility and adoption by agricultural decision-support systems across diverse agroclimatic contexts.