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Related Experiment Video

Updated: Jul 2, 2026

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

Published on: January 9, 2026

Pubescence color classification in soybean breeding using aerial images and the Random Forest machine learning

Osvaldo M Pérez^1,2, Brian W Diers¹, Nicolas F Martin¹

¹Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.

Plant Phenomics (Washington, D.C.)

|July 1, 2026

Summary

This study developed a high-throughput phenotyping method using drone imagery and machine learning to accurately classify soybean pubescence color. The new approach improves upon previous methods, aiding breeding programs in trait selection.

Keywords:

Agriculture High-throughput phenotyping Machine learning Plant breeding Pubescence color UAV Vegetation indices

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Area of Science:

Agricultural Science
Plant Breeding
Remote Sensing

Background:

Soybean pubescence color is a key trait for breeding programs.
Previous high-throughput phenotyping (HTP) methods could not differentiate light tawny from tawny pubescence.
Accurate classification of pubescence color is essential for efficient soybean breeding.

Purpose of the Study:

To develop and test a machine learning model for classifying soybean pubescence color using aerial imagery.
To improve the accuracy of distinguishing between gray, light tawny, and tawny pubescence.
To evaluate the effectiveness of a time-series HTP approach for pubescence color classification.

Main Methods:

Utilized the Random Forest algorithm with time-series aerial RGB and multispectral imagery.
Trained models using data from three years of experiments, incorporating parental line pubescence color.
Tested model performance using data from multiple drone flights before or at maturity.

Main Results:

Achieved an overall accuracy of 86.55% (Kappa = 0.7976) in the best-case scenario.
The red/blue index was the most effective feature for discrimination.
Blue and green normalized difference vegetation index (NDVI) were useful for distinguishing gray from light tawny pubescence.
Models showed lower but fair to good reliability (65.86% accuracy, Kappa = 0.4874) in an independent environment.

Conclusions:

The developed HTP pipeline significantly enhances the efficiency of classifying soybean pubescence color for breeding programs.
Incorporating a time-series of images and parental data improved classification accuracy.
Future improvements could involve genotyping for pod color to mitigate its interference with pubescence color classification.