Data/hora: 12/06/2024 01:04:17

Provedor de dados: | 53
País: | Brazil
Título: | Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce
Autores: | Azevedo,Alcinei Mistico; Andrade Júnior,Valter Carvalho de; Pedrosa,Carlos Enrrik; Oliveira,Celso Mattes de; Dornas,Marcus Flavius Silva; Cruz,Cosme Damião; Valadares,Nermy Ribeiro.
Data: | 2015-12-01
Ano: | 2015
Palavras-chave: | Lactuca sativa; Multi-layer-perceptron; Gain selection; Plant breeding; Computational intelligence.
Resumo: | The efficiency of artificial neural networks (ANN) to model complex problems may enable the prediction of characteristics that are hard to measure, providing better results than the traditional indirect selection. Thus, this study aimed to investigate the potential of using artificial neural networks (ANN) for indirect selection against early flowering in lettuce, identify the influence of genotype by environment interaction in this strategy and compare your results with the traditional indirect selection. The number of days to anthesis were used as the desired output and the information of six characteristics (fresh weight of shoots, mass of marketable fresh matter of shoots, commercial dry matter of shoots, average diameter of the head, head circumference and leaf number) as input file for the training of the ANN-MLP (Perceptron Multi-Layer). The use of ANN has great potential adjustment for indirect selection for genetic improvement of lettuce against early flowering. The selection based on the predicted values by network provided estimates of gain selection largest that traditional indirect selection. The ANN trained with data from an experiment have low power extrapolation to another experiment, due to effect of interaction genotype by environment. The ANNs trained simultaneously with data from different experiments presented greater predictive power and extrapolation.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052015000400387
Editor: | Instituto Agronômico de Campinas
Relação: | 10.1590/1678-4499.0088
Formato: | text/html
Fonte: | Bragantia  v.74 n.4 2015
Fonte: | info:eu-repo/semantics/openAccess