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
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-06, Vol.14 (12), p.2843
Main Authors: Khan, Shahid Nawaz, Li, Dapeng, Maimaitijiang, Maitiniyazi
Format: Article
Language:English
Subjects:
Agricultural production
Algorithms
Belts
Cereal crops
Climate models
Climatic data
Corn
Corn belt
corn yield
Crop yield
Crops
Decision trees
Ethanol
Feature selection
Food security
Learning algorithms
Machine learning
Neural networks
Precipitation
Predictions
Primary production
random forests
Regression
Remote sensing
Soils
spatial autocorrelation
Support vector machines
Vegetation
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Summary: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 Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWRFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWRFR with those of the other five models. The results show that the GWRFR with full length features (R2 = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWRFR also outperforms other models. The Moran’s I value of the residuals generated by GWRFR is smaller than that of other models, which shows that GWRFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14122843