Your browser doesn't support javascript.
loading
Multilayer perceptron applied to genotypes classification in diallel studies
Inocente, Gabriela; Garbuglio, Deoclécio Domingos; Ruas, Paulo Maurício.
Afiliação
  • Inocente, Gabriela; Universidade Estadual de Londrina. Departamento de Agronomia. Londrina. BR
  • Garbuglio, Deoclécio Domingos; Instituto de Desenvolvimento Rural do Paraná. Londrina. BR
  • Ruas, Paulo Maurício; Universidade Estadual de Londrina. Centro de Ciências Biológicas. Londrina. BR
Sci. agric ; 79(3): e20200365, 2022. tab, graf
Article em En | VETINDEX | ID: biblio-1290191
Biblioteca responsável: BR68.1
ABSTRACT
In the last decades, a new trend to use more refined analytical procedures, such as artificial neural networks (ANN), has emerged to be most accurate, efficient, and extensively applied for mining and data prediction in different contexts, including plant breeding. Thus, this study was developed to establish a new classification proposal for targeting genotypes in breeding programs to approach classical models, such as a complete diallel and modern prediction techniques. The study was based on the standard deviation values of an interpopulation diallel and it also verified the possibility of training a neural network with the standardized genetic parameters for a discrete scale. We used 12 intercrossed maize populations in a complete diallel scheme (66 hybrids), evaluated during the 2005/2006 crop season in three different environments in southern Brazil. The implemented MLP architecture and other associated parameters allowed the development of a generalist model of genotype classification. The MLP neural network model was efficient in predicting parental and interpopulation hybrid classifications from average genetic components from a complete diallel, regardless of the evaluation environment.(AU)
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: VETINDEX Idioma: En Revista: Sci. agric Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: VETINDEX Idioma: En Revista: Sci. agric Ano de publicação: 2022 Tipo de documento: Article