A Fuzzy Graph-based Approach for Crop Yield Prediction under Climatic Uncertainty

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. This paper proposes a novel fuzzy graph-based prediction model (FGBPM) that integrates fuzzy set theory with graph-theoretic structures 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 an R² of 0.961, an RMSE of 0.112 t/ha, and an MAE of 0.089 t/ha on the test set, outperforming the ANN, SVM, random forest, and linear regression baselines. 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.