# Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

arXiv: https://arxiv.org/abs/2006.13847 · HTML: https://mlsanity.com/paper/2006.13847/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework Built on GraphSAGE for Optimized Yield Prediction](https://mlsanity.com/paper/2310.13037/) (2023)
- [SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes](https://mlsanity.com/paper/2606.19623/) (2026)
- [Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)](https://mlsanity.com/paper/2202.13541/) (2022)
- [A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection](https://mlsanity.com/paper/2309.13021/) (2023)
- [Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support System](https://mlsanity.com/paper/2401.03768/) (2024)
- [On Convolutional Vision Transformers for Yield Prediction](https://mlsanity.com/paper/2402.05557/) (2024)
- [Sparse Attention Regression Network Based Soil Fertility Prediction With Ummaso](https://mlsanity.com/paper/2404.10274/) (2024)

## Abstract (arXiv)

Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production including erratic rainfall and temperature variations. We used historical performance records from Uniform Soybean Tests (UST) in North America spanning 13 years of data to build a Long Short Term Memory - Recurrent Neural Network based model to dissect and predict genotype response in multiple-environments by leveraging pedigree relatedness measures along with weekly weather parameters. Additionally, for providing explainability of the important time-windows in the growing season, we developed a model based on temporal attention mechanism. The combination of these two models outperformed random forest (RF), LASSO regression and the data-driven USDA model for yield prediction. We deployed this deep learning framework as a 'hypotheses generation tool' to unravel GxExM relationships. Attention-based time series models provide a significant advancement in interpretability of yield prediction models. The insights provided by explainable models are applicable in understanding how plant breeding programs can adapt their approaches for global climate change, for example identification of superior varieties for commercial release, intelligent sampling of testing environments in variety development, and integrating weather parameters for a targeted breeding approach. Using DL models as hypothesis generation tools will enable development of varieties with plasticity response in variable climatic conditions. We envision broad applicability of this approach (via conducting sensitivity analysis and "what-if" scenarios) for soybean and other crop species under different climatic conditions.