A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt

A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt

Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Re...

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Bibliographic Details
Main Authors: Shahid Nawaz Khan, Dapeng Li, Maitiniyazi Maimaitijiang
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Remote Sensing
Subjects:
corn yield
remote sensing
machine learning
random forests
spatial autocorrelation
Online Access:https://www.mdpi.com/2072-4292/14/12/2843

Internet

https://www.mdpi.com/2072-4292/14/12/2843

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