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Related Experiment Video

Updated: Mar 20, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Random Forests for Global and Regional Crop Yield Predictions.

Jig Han Jeong¹, Jonathan P Resop^2,3, Nathaniel D Mueller^4,5

¹School of Environmental and Forest Sciences, College of the Environment, University of Washington, Box 354115, Seattle, WA 98195, United States of America.

|June 4, 2016

Summary

Random Forests (RF) accurately predict crop yields for wheat, maize, and potato globally and regionally. This machine learning method significantly outperformed multiple linear regression (MLR) in yield prediction accuracy.

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Area of Science:

Agricultural Science
Machine Learning
Data Science

Background:

Accurate crop yield prediction is essential for global food security and agricultural policy.
Machine learning (ML) methods offer potential for improved crop yield forecasting compared to traditional statistical models.

Purpose of the Study:

To evaluate the efficacy of Random Forests (RF) for predicting crop yields (wheat, maize, potato) at regional and global scales.
To compare the performance of RF against multiple linear regression (MLR) as a benchmark for crop yield prediction.

Main Methods:

Utilized diverse crop yield datasets for model training and testing, including gridded global wheat, US county-level maize, and northeastern seaboard potato and maize silage data.
Employed Random Forests (RF) and multiple linear regression (MLR) models to predict crop yield responses to climate and biophysical variables.

Main Results:

Random Forests (RF) demonstrated superior performance in crop yield prediction across all tested datasets and regions.
RF models achieved root mean square errors (RMSE) between 6% and 14% of observed yield, significantly outperforming MLR models (14% to 49% RMSE).

Conclusions:

Random Forests (RF) is a highly accurate, precise, and versatile machine learning method for regional and global crop yield prediction.
RF's ease of use and data analysis utility make it a valuable tool for agricultural research and policy.
Potential limitations of RF include reduced accuracy at the extreme ends of data or for predictions beyond training data boundaries.