Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

Abstract 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 hybri...

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Autores principales:	Mohsen Shahhosseini, Guiping Hu, Isaiah Huber, Sotirios V. Archontoulis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b
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spelling	oai:doaj.org-article:33f06b500da54359bd032ea96d19cf9b2021-12-02T14:01:20ZCoupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt10.1038/s41598-020-80820-12045-2322https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80820-1https://doaj.org/toc/2045-2322Abstract 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.Mohsen ShahhosseiniGuiping HuIsaiah HuberSotirios V. ArchontoulisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohsen Shahhosseini Guiping Hu Isaiah Huber Sotirios V. Archontoulis Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
description	Abstract 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	article
author	Mohsen Shahhosseini Guiping Hu Isaiah Huber Sotirios V. Archontoulis
author_facet	Mohsen Shahhosseini Guiping Hu Isaiah Huber Sotirios V. Archontoulis
author_sort	Mohsen Shahhosseini
title	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_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_sort	coupling machine learning and crop modeling improves crop yield prediction in the us corn belt
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b
work_keys_str_mv	AT mohsenshahhosseini couplingmachinelearningandcropmodelingimprovescropyieldpredictionintheuscornbelt AT guipinghu couplingmachinelearningandcropmodelingimprovescropyieldpredictionintheuscornbelt AT isaiahhuber couplingmachinelearningandcropmodelingimprovescropyieldpredictionintheuscornbelt AT sotiriosvarchontoulis couplingmachinelearningandcropmodelingimprovescropyieldpredictionintheuscornbelt
_version_	1718392190485397504

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

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