Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

TL;DR

This paper presents a deep learning framework combining LSTM and attention mechanisms to predict soybean crop yields by integrating genotype and weather data, offering improved accuracy and interpretability for breeding and climate adaptation.

Contribution

The study introduces a novel deep learning approach that combines LSTM and temporal attention for crop yield prediction, enhancing interpretability and outperforming traditional models.

Findings

Deep learning models outperform traditional methods in yield prediction.

Attention mechanisms identify key time-windows affecting crop yield.

Explainability aids in understanding genotype-environment interactions.

Abstract

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.