Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions
This study investigated how genotype, environment, and management (G×E×M) interactions influence yield and highlight the importance of accurate, early yield predictions for effective farm management and enhancing food security. We developed a yield prediction model capable of determining field-level...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1537990/full |
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| author | Saiara Samira Sajid Zahra Khalilzadeh Lizhi Wang Guiping Hu |
| author_facet | Saiara Samira Sajid Zahra Khalilzadeh Lizhi Wang Guiping Hu |
| author_sort | Saiara Samira Sajid |
| collection | DOAJ |
| description | This study investigated how genotype, environment, and management (G×E×M) interactions influence yield and highlight the importance of accurate, early yield predictions for effective farm management and enhancing food security. We developed a yield prediction model capable of determining field-level outputs based on comprehensive data inputs, including genotype, spatial, temporal, environmental, and management factors. Among tested models—LASSO, Random Forest, XGBoost, single-modal CNN-DNN, and multimodal CNN-DNN—the multimodal CNN-DNN ensembled with XGBoost demonstrated superior performance. Applied to the G2F dataset covering 21 states from 2014 to 2021 across various treatments (i.e., standard, drought, irrigation, disease trials), the model excelled particularly in stable historical yield settings (RMSE 2.36 Mg/ha for standard treatment) with an overall RMSE of 2.45 Mg/ha. Additionally, we introduced an empirical tool for identifying high-yield hybrids suitable for standard and challenging conditions. Exploratory analysis confirmed that crop yields vary greatly by hybrid and location interaction and that late planting generally yields less than standard timing. Customized management strategies based on specific local and hybrid conditions are crucial for optimal yield outcomes. |
| format | Article |
| id | doaj-art-6da1bb3a139143598d93bf65f48d70e4 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-6da1bb3a139143598d93bf65f48d70e42025-08-20T03:31:40ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.15379901537990Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactionsSaiara Samira Sajid0Zahra Khalilzadeh1Lizhi Wang2Guiping Hu3Iowa State University, Industrial Manufacturing & Systems Engineering, Ames, IA, United StatesIowa State University, Industrial Manufacturing & Systems Engineering, Ames, IA, United StatesOklahoma State University, Industrial Engineering & Management, Stillwater, OK, United StatesOklahoma State University, Industrial Engineering & Management, Stillwater, OK, United StatesThis study investigated how genotype, environment, and management (G×E×M) interactions influence yield and highlight the importance of accurate, early yield predictions for effective farm management and enhancing food security. We developed a yield prediction model capable of determining field-level outputs based on comprehensive data inputs, including genotype, spatial, temporal, environmental, and management factors. Among tested models—LASSO, Random Forest, XGBoost, single-modal CNN-DNN, and multimodal CNN-DNN—the multimodal CNN-DNN ensembled with XGBoost demonstrated superior performance. Applied to the G2F dataset covering 21 states from 2014 to 2021 across various treatments (i.e., standard, drought, irrigation, disease trials), the model excelled particularly in stable historical yield settings (RMSE 2.36 Mg/ha for standard treatment) with an overall RMSE of 2.45 Mg/ha. Additionally, we introduced an empirical tool for identifying high-yield hybrids suitable for standard and challenging conditions. Exploratory analysis confirmed that crop yields vary greatly by hybrid and location interaction and that late planting generally yields less than standard timing. Customized management strategies based on specific local and hybrid conditions are crucial for optimal yield outcomes.https://www.frontiersin.org/articles/10.3389/fpls.2025.1537990/fullgenotypeplanting datehigh-yield hybrid classificationprecision farmingmultimodal CNN-DNN |
| spellingShingle | Saiara Samira Sajid Zahra Khalilzadeh Lizhi Wang Guiping Hu Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions Frontiers in Plant Science genotype planting date high-yield hybrid classification precision farming multimodal CNN-DNN |
| title | Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions |
| title_full | Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions |
| title_fullStr | Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions |
| title_full_unstemmed | Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions |
| title_short | Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions |
| title_sort | analyses of crop yield dynamics and the development of a multimodal neural network prediction model with g e m interactions |
| topic | genotype planting date high-yield hybrid classification precision farming multimodal CNN-DNN |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1537990/full |
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