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 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%.

SUBMITTER: Shahhosseini M 

PROVIDER: S-EPMC7810832 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature