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Agriculture, as the cornerstone of human civilization, constantly seeks tointegrate technology for enhanced productivity and sustainability. This paperintroduces $\textit{Agri-GNN}$, a novel Genotypic-Topological Graph NeuralNetwork Framework tailored to capture the intricate spatial and genotypicinteractions of crops, paving the way for optimized predictions of harvestyields. $\textit{Agri-GNN}$ constructs a Graph $\mathcal{G}$ that considersfarming plots as nodes, and then methodically constructs edges between nodesbased on spatial and genotypic similarity, allowing for the aggregation of nodeinformation through a genotypic-topological filter. Graph Neural Networks(GNN), by design, consider the relationships between data points, enabling themto efficiently model the interconnected agricultural ecosystem. By harnessingthe power of GNNs, $\textit{Agri-GNN}$ encapsulates both local and globalinformation from plants, considering their inherent connections based onspatial proximity and shared genotypes, allowing stronger predictions to bemade than traditional Machine Learning architectures. $\textit{Agri-GNN}$ isbuilt from the GraphSAGE architecture, because of its optimal calibration withlarge graphs, like those of farming plots and breeding experiments.$\textit{Agri-GNN}$ experiments, conducted on a comprehensive dataset ofvegetation indices, time, genotype information, and location data, demonstratethat $\textit{Agri-GNN}$ achieves an $R^2 = .876$ in yield predictions forfarming fields in Iowa. The results show significant improvement over thebaselines and other work in the field. $\textit{Agri-GNN}$ represents ablueprint for using advanced graph-based neural architectures to predict cropyield, providing significant improvements over baselines in the field.

Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework Built on GraphSAGE for Optimized Yield Prediction