Campus Units
Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2020
Journal or Book Title
arXiv
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.
Copyright Owner
The Author(s)
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Shook, Johnathon; Gangopadhyay, Tryambak; Wu, Linjiang; Ganapathysubramanian, Baskar; Sarkar, Soumik; and Singh, Asheesh K., "Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning" (2020). Mechanical Engineering Publications. 424.
https://lib.dr.iastate.edu/me_pubs/424
Included in
Agronomy and Crop Sciences Commons, Artificial Intelligence and Robotics Commons, Climate Commons, Plant Breeding and Genetics Commons
Comments
This is a pre-print of the article Shook, Johnathon, Tryambak Gangopadhyay, Linjiang Wu, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh K. Singh. "Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning." arXiv preprint arXiv:2006.13847 (2020). Posted with permission.