Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Bragantia |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052015000400387 |
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. |
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Application of artificial neural networks in indirect selection: a case study on the breeding of lettuceLactuca sativamulti-layer-perceptrongain selectionplant breedingcomputational intelligenceThe 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.Instituto Agronômico de Campinas2015-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052015000400387Bragantia v.74 n.4 2015reponame:Bragantiainstname:Instituto Agronômico de Campinas (IAC)instacron:IAC10.1590/1678-4499.0088info:eu-repo/semantics/openAccessAzevedo,Alcinei MisticoAndrade Júnior,Valter Carvalho dePedrosa,Carlos EnrrikOliveira,Celso Mattes deDornas,Marcus Flavius SilvaCruz,Cosme DamiãoValadares,Nermy Ribeiroeng2015-10-26T00:00:00Zoai:scielo:S0006-87052015000400387Revistahttps://www.scielo.br/j/brag/https://old.scielo.br/oai/scielo-oai.phpbragantia@iac.sp.gov.br||bragantia@iac.sp.gov.br1678-44990006-8705opendoar:2015-10-26T00:00Bragantia - Instituto Agronômico de Campinas (IAC)false |
dc.title.none.fl_str_mv |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
title |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
spellingShingle |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce Azevedo,Alcinei Mistico Lactuca sativa multi-layer-perceptron gain selection plant breeding computational intelligence |
title_short |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
title_full |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
title_fullStr |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
title_full_unstemmed |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
title_sort |
Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce |
author |
Azevedo,Alcinei Mistico |
author_facet |
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 |
author_role |
author |
author2 |
Andrade Júnior,Valter Carvalho de Pedrosa,Carlos Enrrik Oliveira,Celso Mattes de Dornas,Marcus Flavius Silva Cruz,Cosme Damião Valadares,Nermy Ribeiro |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
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 |
dc.subject.por.fl_str_mv |
Lactuca sativa multi-layer-perceptron gain selection plant breeding computational intelligence |
topic |
Lactuca sativa multi-layer-perceptron gain selection plant breeding computational intelligence |
description |
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. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052015000400387 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052015000400387 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4499.0088 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto Agronômico de Campinas |
publisher.none.fl_str_mv |
Instituto Agronômico de Campinas |
dc.source.none.fl_str_mv |
Bragantia v.74 n.4 2015 reponame:Bragantia instname:Instituto Agronômico de Campinas (IAC) instacron:IAC |
instname_str |
Instituto Agronômico de Campinas (IAC) |
instacron_str |
IAC |
institution |
IAC |
reponame_str |
Bragantia |
collection |
Bragantia |
repository.name.fl_str_mv |
Bragantia - Instituto Agronômico de Campinas (IAC) |
repository.mail.fl_str_mv |
bragantia@iac.sp.gov.br||bragantia@iac.sp.gov.br |
_version_ |
1754193304428216320 |