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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Computational intelligence to study the importance of characteristics in flood-irrigated rice

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Author(s):
Antônio Carlos da Silva Júnior [1] ; Isabela Castro SantAnna [2] ; Gabi Nunes Silva [3] ; Cosme Damião Cruz [4] ; Moysés Nascimento [5] ; Leonardo Bhering Lopes [6] ; Plínio César Soares [7]
Total Authors: 7
Affiliation:
[1] Universidade Federal de Viçosa. Departamento de Biologia Geral - Brasil
[2] Instituto Agronômico. Centro de Seringueira e Sistemas Agroflorestais - Brasil
[3] Universidade Federal de Rondônia. Departamento Acadêmico de Matemática e Estatística - Brasil
[4] Universidade Federal de Viçosa. Departamento de Biologia Geral - Brasil
[5] Universidade Federal de Viçosa. Departamento de Estatística - Brasil
[6] Universidade Federal de Viçosa. Departamento de Biologia Geral - Brasil
[7] Empresa de Pesquisa Agropecuária de Minas Gerais - Brasil
Total Affiliations: 7
Document type: Journal article
Source: Acta Scientiarum. Agronomy; v. 45, 2023-03-03.
Abstract

ABSTRACT. The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice. (AU)

FAPESP's process: 18/26408-0 - GENETIC DIVERSITY AND DESCRIPTORS CHARACTERIZATION OF RUBBER TREE
Grantee:Isabela de Castro Sant'Anna
Support Opportunities: Scholarships in Brazil - Post-Doctoral