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Updated: Nov 1, 2025
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
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Crop yield prediction integrating genotype and weather variables using deep learning
Johnathon Shook1, Tryambak Gangopadhyay2, Linjiang Wu2
1Department of Agronomy, Iowa State University, Ames, IA, United States of America.
Plos One
|June 17, 2021
Summary
Accurate crop yield prediction using a Long Short-Term Memory (LSTM)-Recurrent Neural Network model enhances agricultural breeding. This AI approach integrates genetic data and weather patterns for improved crop production strategies.
Area of Science:
- Agricultural Science
- Computational Biology
- Genetics
Background:
- Accurate crop yield prediction is crucial for agricultural breeding and managing climate-related challenges.
- Existing models may not fully capture complex genotype-environment interactions.
Purpose of the Study:
- To develop and validate an advanced predictive model for crop yield using machine learning.
- To enhance genotype response prediction by integrating genetic relatedness and environmental data.
Main Methods:
- Utilized Uniform Soybean Tests (UST) performance records from North America.
- Developed a Long Short-Term Memory (LSTM)-Recurrent Neural Network model incorporating pedigree relatedness and weekly weather data.
- Implemented a temporal attention mechanism for model interpretability.
Main Results:
- The proposed LSTM-Recurrent Neural Network model demonstrated superior performance in yield prediction compared to Support Vector Regression with Radial Basis Function kernel (SVR-RBF), LASSO regression, and the USDA model.
- The temporal attention mechanism identified critical growth period windows influencing yield.
Conclusions:
- The developed interpretable LSTM model accurately predicts soybean yield across multiple environments.
- This approach offers valuable insights for plant breeders to optimize crop development and resilience.
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