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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the

Sayantan Sarkar¹, Javier M Osorio Leyton¹, Efrain Noa-Yarasca¹

¹Texas A&M AgriLife Blackland Research and Extension Center, Temple, TX 76502, USA.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

Accurate corn yield prediction in production fields is possible using machine learning models. Integrating soil properties and vegetation indices at the V14/VT growth stage with the random forest model offers the best results for farmers.

Keywords:

corn ensemble methods machine learning maize vegetation indices yield prediction

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Area of Science:

Agricultural Science
Remote Sensing
Machine Learning

Background:

Corn yield prediction is crucial for agricultural management and varietal selection.
Previous studies often lack real-world applicability, focusing on smaller or controlled areas.
This research addresses yield prediction in a production-scale, rain-fed environment.

Purpose of the Study:

To identify optimal vegetation indices and abiotic factors for corn yield prediction.
To determine the most effective corn growth stage for yield estimation using machine learning.
To evaluate the performance of different machine learning models for corn yield prediction.

Main Methods:

Utilized high-resolution (6 cm) aerial multispectral imagery.
Derived 62 predictors including soil properties, slope, spectral bands, and vegetation indices (e.g., GNDRE, NDRE, TGI) across seven corn growth stages (V4-V14/VT).
Evaluated four machine learning algorithms: linear regression, random forest, extreme gradient boosting, and gradient boosting regressor.

Main Results:

The random forest model at the V14/VT growth stage achieved the highest accuracy (RMSE of 0.52 Mg/ha).
Yield estimation at the V6 stage was also found to be feasible.
Integration of abiotic factors (slope, soil properties) and specific vegetation indices (TGI, HUE, GNDRE) significantly improved prediction accuracy.

Conclusions:

Machine learning models, particularly random forest, combined with abiotic factors and vegetation indices, can accurately predict corn yield at a production scale.
Early-season yield estimation is feasible, aiding farmers and crop consultants in planning and decision-making.
The findings support enhanced farm profitability and sustainability through improved yield prediction.