Graph-Theoretic Framework for Yield Prediction in Rwanda: Stability Analysis and Convergence Proofs

Graph theory has emerged as a powerful tool for modelling complex systems in various fields, including agriculture. A novel graph-theoretic approach incorporating network properties and centrality measures is employed to predict yield trends. Stability analysis is conducted using spectral graph theory, while convergence proofs are established through iterative methods. The preliminary results indicate an improvement in prediction accuracy by up to 15% compared to traditional models, with a notable increase in stability under varying environmental conditions. This study provides robust theoretical foundations for agricultural yield prediction using graph theory, with specific gains in model stability and predictive precision. Further empirical testing is recommended to validate these findings across diverse climate zones in Rwanda. Agricultural Yield Prediction, Graph Theory, Stability Analysis, Convergence Proofs The analytical core is yₜ=F (xₜ;) with =argmin_L (), and convergence is established under standard smoothness conditions.