Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework
  Built on GraphSAGE for Optimized Yield Prediction
Aditya Gupta Asheesh Singh
Agriculture, as the cornerstone of human civilization, constantly seeks to
integrate technology for enhanced productivity and sustainability. This paper
introduces $\textit{Agri-GNN}$, a novel Genotypic-Topological Graph Neural
Network Framework tailored to capture the intricate spatial and genotypic
interactions of crops, paving the way for optimized predictions of harvest
yields. $\textit{Agri-GNN}$ constructs a Graph $\mathcal{G}$ that considers
farming plots as nodes, and then methodically constructs edges between nodes
based on spatial and genotypic similarity, allowing for the aggregation of node
information through a genotypic-topological filter. Graph Neural Networks
(GNN), by design, consider the relationships between data points, enabling them
to efficiently model the interconnected agricultural ecosystem. By harnessing
the power of GNNs, $\textit{Agri-GNN}$ encapsulates both local and global
information from plants, considering their inherent connections based on
spatial proximity and shared genotypes, allowing stronger predictions to be
made than traditional Machine Learning architectures. $\textit{Agri-GNN}$ is
built from the GraphSAGE architecture, because of its optimal calibration with
large graphs, like those of farming plots and breeding experiments.
$\textit{Agri-GNN}$ experiments, conducted on a comprehensive dataset of
vegetation indices, time, genotype information, and location data, demonstrate
that $\textit{Agri-GNN}$ achieves an $R^2 = .876$ in yield predictions for
farming fields in Iowa. The results show significant improvement over the
baselines and other work in the field. $\textit{Agri-GNN}$ represents a
blueprint for using advanced graph-based neural architectures to predict crop
yield, providing significant improvements over baselines in the field.