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Updated: Aug 10, 2025

Author Spotlight: Innovative Approaches to Understanding Plant Structure-Function Relationships for Climate-Resilient Crops

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Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting

Paulina Ballesta¹, Carlos Maldonado², Freddy Mora-Poblete³

¹Instituto de Nutrición y Tecnología de Los Alimentos, Universidad de Chile, Santiago 7830490, Chile.

Plants (Basel, Switzerland)

|February 11, 2023

Summary

Foliar spectral data accurately identifies wheat subpopulations, even across different water conditions. This spectral-based classification using machine learning aids crop genetic resources management.

Keywords:

Triticum aestivum foliar reflectance genetic structure remote sensing classification

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Published on: August 8, 2017

Area of Science:

Genetics and Breeding
Plant Physiology
Agricultural Science

Background:

Global food security necessitates advancements in cereal crop genetics and breeding.
Understanding wheat genetic structure is crucial for effective germplasm management and crop improvement.
Previous studies assessed germplasm sustainability, but spectral-based genetic classification remains underexplored.

Purpose of the Study:

To develop and evaluate a spectral-based classification approach for assigning wheat cultivars to genetically distinct subpopulations.
To assess the accuracy of machine learning models in predicting genetic structure using foliar spectral data under varying water regimes.
To determine the stability and reliability of spectral-based classification across different environmental conditions.

Main Methods:

A panel of 316 spring bread wheat cultivars was analyzed.
Foliar spectral data and genetic information were collected from cultivars grown in rainfed and fully irrigated environments.
Machine learning models, including Convolutional Neural Network (CNN), multilayer perceptron, and partial least squares discriminant analysis, were trained and compared.

Main Results:

Convolutional Neural Network (CNN) demonstrated superior accuracy (92-93%) in classifying wheat cultivars into subpopulations across both water regimes.
Spectral differences between genetically differentiated groups were less pronounced under rainfed conditions, impacting clustering accuracy.
CNN showed stable prediction performance irrespective of water availability, unlike other models.

Conclusions:

Foliar spectral variation is a reliable indicator for inferring cultivar belonging to genetically differentiated groups.
Spectral-based classification offers a stable and accurate method for crop genetic resources management, adaptable to diverse environments.
This approach holds significant promise for enhancing the efficiency of wheat breeding programs and germplasm utilization.