Trait prediction through computational intelligence and machine learning applied to the improvement of white oat (Avena sativa L)

Aug 4, 2026

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Trait prediction enables breeders to steer selection strategies and accelerate genetic improvement. This study aimed to identify the most effective prediction approach and build a network with higher predictive power for white oat using artificial intelligence and machine learning. Seventy-eight genotypes were evaluated in randomized blocks with three replications, with model tests performed with and without fungicide, using grain yield as the response trait and other traits as predictors. R2 was used to assess performance, including trait-importance estimation by information disruption effects. Decision trees, bagging, random forest, and boosting were applied, with bagging giving the highest R2 estimates (30.14–96.45% and 10.57–94.61% ranges).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

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Abstract

Study objective and approach

Experimental design and predictors

Models and evaluation metrics

Plant Breeding Applied to Agriculture [https://doi.org/10.1590/0034-737X2024710045](https://doi.org/10.1590/0034-737X2024710045) ISSN: 2177-3491 Trait prediction through computational intelligence and machine learning applied to the improvement of white oat (Avena sativa L) Antônio Carlos da Silva Júnior1* , Isabela Castro Sant’Anna2 , Michele Jorge da Silva1 , Leonardo Lopes Bhering3 , Moysés Nascimento3 , Ivan Ricardo Carvalho4 , José Antônio Gonzalez da Silva4 , Cosme Damião Cruz3 1 Universidade Federal de Viçosa, Departamento de Biologia Geral, Viçosa, MG, Brazil. michele[jorgesilva@gmail.com](jorgesilva@gmail.com), [leonardo.bhering@ufv.br](leonardo.bhering@ufv.br), [cdcruz@ufv.br](cdcruz@ufv.br) 2 Instituto Agronômico (IAC), Centro de Seringueira e Sistemas Agroflorestais, Votuporanga, SP, Bra[zil. isabelacsantanna@gmail.com](zil. isabelacsantanna@gmail.com) 3 Universidade Federal de Viçosa, Departamento de Estatística, Viçosa, MG, Brazil. moysesnascim@ [gmail.com](gmail.com) 4 Universidade Regional do Noroeste do Estado do Rio Grande do Sul, Ijuí, RS, Brazil. ivan.carvalho@ [unijui.edu.br](unijui.edu.br), [jose.gonzales@unijui.edu.br](jose.gonzales@unijui.edu.br) *Corresponding author: antonio.silva.c.junior@ [gmail.com](gmail.com) Editors: Teogenes Senna de Oliveira Submitted: May 17th, 2023. Accepted: August 30th, 2024. ABSTRACT The prediction of traits allows the breeder to guide strategies to select and accelerate the progress of genetic improvement. The objective of this work was to determine the best prediction approach and establish a network with better predictive power for white oat using methodologies based on artificial intelligence, and machine learning. Seventy-eight white oat genotypes were evaluated. The design was randomized blocks with three replications. The models were evaluated with and without fungicide, and prediction models were established using four sets of experiments. The grain yield was used as a response trait the others as explanatory traits. The coefficient of determination was considered to evaluate the proposed methodologies. The importance of the traits was assessed through the impact of destructuring or disturbing the information of a given input on the estimation of R2. For machine learning, decision trees, bagging, random forest, and boosting were used. The traits indicated to assist indecision-making are plant height, leaf rust severity, and lodging percentage. The R2 ranged from 30. 14% -96.45% and 10.57% -94.61% for computational intelligence and machine learning, respectively. A high estimate of the coefficient of determination, which was larger than the other estimates, was obtained using the bagging technique. Keywords: Avena sativa L.; multiple regression; decision trees; Artificial neural networks. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original work is properly cited. Rev. Ceres, Viçosa, v. 71, e71045, 2024 INTRODUCTION White oats (Avena sativa L.) are of great agricultural importance worldwide. Brazil is the fifth-largest producer globally and has experienced a substantial increase in areas cultivated with white oats in the last ten years (Conab, 2022) . This crop can be used to produce grain, forage, and straw in a no-tillage system (Corazza et al., 2021) . Oat forage is preferred over other annual forage crops because of its high palatability and dry matter content (McCartney et al., 2008; Kim et al., 2014; Sharma et al., 2022) . Estimating the importance of predictor traits in breeding programs allows for faster progress and selecting and predicting traits with low heritability and/or measurement difficulty (Silva Junior et al., 2021 and 2023). Although the simultaneous assessment of traits provides a wide variety of information, identifying which predictor trait

FAQs

What was the main goal of this white oat study?

To determine the best trait-prediction approach and develop predictive networks with stronger predictive power for improving white oat using artificial intelligence and machine learning.

How were the oat genotypes evaluated in the experiment?

Seventy-eight genotypes of white oat were tested using a randomized blocks design with three replications, and prediction models were evaluated with and without fungicide.

Which traits were found to support decision-making for breeders?

Plant height, leaf rust severity, and lodging percentage were identified as traits assisting indecision-making.

Aug 4, 2026

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