Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality
Ensuring accurate predictions of wheat yield and nutritional content is vital for enhancing agricultural pro ductivity and food security. This study aims to improve wheat yield prediction by integrating process-based models (PBM), machine learning (ML), and remote sensing (RS) techniques. Three Dec...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/175162 |
| _version_ | 1855533425329438720 |
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| author | Kheir, Ahmed M.S. Govind, Ajit Nangia, Vinay El-Maghraby, Maher A. Elnashar, Abdelrazek Ahmed, Mukhtar Aboelsoud, Hesham Mostafa, Rania Feike, Til |
| author_browse | Aboelsoud, Hesham Ahmed, Mukhtar El-Maghraby, Maher A. Elnashar, Abdelrazek Feike, Til Govind, Ajit Kheir, Ahmed M.S. Mostafa, Rania Nangia, Vinay |
| author_facet | Kheir, Ahmed M.S. Govind, Ajit Nangia, Vinay El-Maghraby, Maher A. Elnashar, Abdelrazek Ahmed, Mukhtar Aboelsoud, Hesham Mostafa, Rania Feike, Til |
| author_sort | Kheir, Ahmed M.S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Ensuring accurate predictions of wheat yield and nutritional content is vital for enhancing agricultural pro
ductivity and food security. This study aims to improve wheat yield prediction by integrating process-based
models (PBM), machine learning (ML), and remote sensing (RS) techniques. Three Decision Support System
for Agrotechnology Transfer (DSSAT) wheat models were calibrated and evaluated using field data from three
wheat cultivars grown over three seasons in diverse environments. We developed a hybrid PBM-ML-RS approach
using polynomial regression to generate iron (Fe) and zinc (Zn) content from nitrogen predictions. The DSSAT
wheat models slightly overestimated wheat yield but accurately predicted nitrogen content. The hybrid PBM-ML-
RS approach closely estimated Fe and Zn content with a root mean square error (RMSE) of 0.42 t/ha for yield and
0.89 % for nitrogen content. The integration of ML and RS improved the prediction accuracy for Fe and Zn,
achieving RMSE values of 0.35 % and 0.28 % respectively. Spatial simulations provided detailed geographic
estimations of wheat yield and nutrient content, supporting site-specific management practices. This study
demonstrates the potential of combining PBM, ML, and RS for comprehensive yield and nutrition prediction. The
f
indings indicate a modest decrease in protein, Fe, and Zn concentrations with increasing grain yield, exhibiting
high variability across different sites and cultivars. Future research should integrate additional data sources to
enhance model robustness and applicability to other crops and regions, contributing to sustainable agriculture
and food security. |
| format | Journal Article |
| id | CGSpace175162 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1751622026-01-22T02:11:30Z Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality Kheir, Ahmed M.S. Govind, Ajit Nangia, Vinay El-Maghraby, Maher A. Elnashar, Abdelrazek Ahmed, Mukhtar Aboelsoud, Hesham Mostafa, Rania Feike, Til uncertainty zinc iron wheat dssat protein random forest regressor nutrient concentration Ensuring accurate predictions of wheat yield and nutritional content is vital for enhancing agricultural pro ductivity and food security. This study aims to improve wheat yield prediction by integrating process-based models (PBM), machine learning (ML), and remote sensing (RS) techniques. Three Decision Support System for Agrotechnology Transfer (DSSAT) wheat models were calibrated and evaluated using field data from three wheat cultivars grown over three seasons in diverse environments. We developed a hybrid PBM-ML-RS approach using polynomial regression to generate iron (Fe) and zinc (Zn) content from nitrogen predictions. The DSSAT wheat models slightly overestimated wheat yield but accurately predicted nitrogen content. The hybrid PBM-ML- RS approach closely estimated Fe and Zn content with a root mean square error (RMSE) of 0.42 t/ha for yield and 0.89 % for nitrogen content. The integration of ML and RS improved the prediction accuracy for Fe and Zn, achieving RMSE values of 0.35 % and 0.28 % respectively. Spatial simulations provided detailed geographic estimations of wheat yield and nutrient content, supporting site-specific management practices. This study demonstrates the potential of combining PBM, ML, and RS for comprehensive yield and nutrition prediction. The f indings indicate a modest decrease in protein, Fe, and Zn concentrations with increasing grain yield, exhibiting high variability across different sites and cultivars. Future research should integrate additional data sources to enhance model robustness and applicability to other crops and regions, contributing to sustainable agriculture and food security. 2025-03-23 2025-06-18T16:27:31Z 2025-06-18T16:27:31Z Journal Article https://hdl.handle.net/10568/175162 en Open Access application/pdf Elsevier Ahmed M. S. Kheir, Ajit Govind, Vinay Nangia, Maher A. El-Maghraby, Abdelrazek Elnashar, Mukhtar Ahmed, Hesham Aboelsoud, Rania Mostafa, Til Feike. (23/3/2025). Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality. Computers and Electronics in Agriculture, 234. |
| spellingShingle | uncertainty zinc iron wheat dssat protein random forest regressor nutrient concentration Kheir, Ahmed M.S. Govind, Ajit Nangia, Vinay El-Maghraby, Maher A. Elnashar, Abdelrazek Ahmed, Mukhtar Aboelsoud, Hesham Mostafa, Rania Feike, Til Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality |
| title | Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality |
| title_full | Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality |
| title_fullStr | Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality |
| title_full_unstemmed | Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality |
| title_short | Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality |
| title_sort | hybridization of process based models remote sensing and machine learning for enhanced spatial predictions of wheat yield and quality |
| topic | uncertainty zinc iron wheat dssat protein random forest regressor nutrient concentration |
| url | https://hdl.handle.net/10568/175162 |
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