ArticlePloS one2021
Crop yield prediction integrating genotype and weather variables using deep learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.PLoS computational biology · 2026Article
- Predicting enviromically adapted varieties with big data.Genome biology · 2026Article
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction.Frontiers in artificial intelligence · 2026Article
- AI-enabled Barilai-Borwein-Blinder-Oaxaca-Bernoulli Deep Classifier for Enhanced Crop Yield Prediction.Scientific reports · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Article
- A data-driven crop model for biomass sorghum growth process simulation.Frontiers in plant science · 2025Article
- Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.Forestry research · 2025Article
- Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions.Frontiers in plant science · 2025Article
- Review
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.Genetics · 2024Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Case study on climate change effects and food security in Southeast Asia.Scientific reports · 2024Article
- Cleaned Meta Pseudo Labels-Based Pet Behavior Recognition Using Time-Series Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.Scientific reports · 2024Article
- Multi-source information fusion-driven corn yield prediction using the Random Forest from the perspective of Agricultural and Forestry Economic Management.Scientific reports · 2024Article
- Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress.BMC plant biology · 2024Article
- Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)-Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.