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

arXiv: https://arxiv.org/abs/2310.13037 · HTML: https://mlsanity.com/paper/2310.13037/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections](https://mlsanity.com/paper/2106.03306/) (2021)
- [Crop Yield Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1902.02860/) (2019)
- [A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction](https://mlsanity.com/paper/2111.08900/) (2021)
- [Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications](https://mlsanity.com/paper/2011.07118/) (2020)
- [Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning](https://mlsanity.com/paper/2006.13847/) (2020)

## Built on by (most cited first, 3 of 3)

- [SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes](https://mlsanity.com/paper/2606.19623/) (2026)
- [Robust soybean seed yield estimation using high-throughput ground robot videos](https://mlsanity.com/paper/2412.02642/) (2024)
- [Spatio-Temporal Graph Neural Networks for Dairy Farm Sustainability Forecasting and Counterfactual Policy Analysis](https://mlsanity.com/paper/2512.19970/) (2025)

## Abstract (arXiv)

Agriculture, as the cornerstone of human civilization, constantly seeks to integrate technology for enhanced productivity and sustainability. This paper introduces textitAgri-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. textitAgri-GNN constructs a Graph 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, textitAgri-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. textitAgri-GNN is built from the GraphSAGE architecture, because of its optimal calibration with large graphs, like those of farming plots and breeding experiments. textitAgri-GNN experiments, conducted on a comprehensive dataset of vegetation indices, time, genotype information, and location data, demonstrate that textitAgri-GNN achieves an R2 = .876 in yield predictions for farming fields in Iowa. The results show significant improvement over the baselines and other work in the field. textitAgri-GNN represents a blueprint for using advanced graph-based neural architectures to predict crop yield, providing significant improvements over baselines in the field.