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Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810832/ https://www.ncbi.nlm.nih.gov/pubmed/33452349 http://dx.doi.org/10.1038/s41598-020-80820-1 |
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author | Shahhosseini, Mohsen Hu, Guiping Huber, Isaiah Archontoulis, Sotirios V. |
author_facet | Shahhosseini, Mohsen Hu, Guiping Huber, Isaiah Archontoulis, Sotirios V. |
author_sort | Shahhosseini, Mohsen |
collection | PubMed |
description | This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions. |
format | Online Article Text |
id | pubmed-7810832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78108322021-01-21 Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt Shahhosseini, Mohsen Hu, Guiping Huber, Isaiah Archontoulis, Sotirios V. Sci Rep Article This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions. Nature Publishing Group UK 2021-01-15 /pmc/articles/PMC7810832/ /pubmed/33452349 http://dx.doi.org/10.1038/s41598-020-80820-1 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shahhosseini, Mohsen Hu, Guiping Huber, Isaiah Archontoulis, Sotirios V. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt |
title | Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt |
title_full | Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt |
title_fullStr | Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt |
title_full_unstemmed | Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt |
title_short | Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt |
title_sort | coupling machine learning and crop modeling improves crop yield prediction in the us corn belt |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810832/ https://www.ncbi.nlm.nih.gov/pubmed/33452349 http://dx.doi.org/10.1038/s41598-020-80820-1 |
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