Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Ferreira, Mariane Gonçalves
Data de Publicação: 2017
Outros Autores: Azevedo, Alcinei Mistico, Siman, Luhan Isaac, da Silva, Gustavo Henrique, Carneiro, Clebson dos Santos, Alves, Flávia Maria, Delazari, Fábio Teixeira, da Silva, Derly José Henriques, Nick, Carlos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/130943
Resumo: Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.
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spelling Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networksCapsicum spp.Garson’s methodartificial intelligencetaxonomygermplasm bankGermplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2017-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/13094310.1590/1678-992x-2015-0451Scientia Agricola; v. 74 n. 3 (2017); 203-207Scientia Agricola; Vol. 74 Núm. 3 (2017); 203-207Scientia Agricola; Vol. 74 No. 3 (2017); 203-2071678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/130943/127402Copyright (c) 2017 Scientia Agricolainfo:eu-repo/semantics/openAccessFerreira, Mariane GonçalvesAzevedo, Alcinei MisticoSiman, Luhan Isaacda Silva, Gustavo HenriqueCarneiro, Clebson dos SantosAlves, Flávia MariaDelazari, Fábio Teixeirada Silva, Derly José HenriquesNick, Carlos2017-05-22T17:04:28Zoai:revistas.usp.br:article/130943Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2017-05-22T17:04:28Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
title Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
spellingShingle Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
Ferreira, Mariane Gonçalves
Capsicum spp.
Garson’s method
artificial intelligence
taxonomy
germplasm bank
title_short Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
title_full Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
title_fullStr Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
title_full_unstemmed Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
title_sort Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
author Ferreira, Mariane Gonçalves
author_facet Ferreira, Mariane Gonçalves
Azevedo, Alcinei Mistico
Siman, Luhan Isaac
da Silva, Gustavo Henrique
Carneiro, Clebson dos Santos
Alves, Flávia Maria
Delazari, Fábio Teixeira
da Silva, Derly José Henriques
Nick, Carlos
author_role author
author2 Azevedo, Alcinei Mistico
Siman, Luhan Isaac
da Silva, Gustavo Henrique
Carneiro, Clebson dos Santos
Alves, Flávia Maria
Delazari, Fábio Teixeira
da Silva, Derly José Henriques
Nick, Carlos
author2_role author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ferreira, Mariane Gonçalves
Azevedo, Alcinei Mistico
Siman, Luhan Isaac
da Silva, Gustavo Henrique
Carneiro, Clebson dos Santos
Alves, Flávia Maria
Delazari, Fábio Teixeira
da Silva, Derly José Henriques
Nick, Carlos
dc.subject.por.fl_str_mv Capsicum spp.
Garson’s method
artificial intelligence
taxonomy
germplasm bank
topic Capsicum spp.
Garson’s method
artificial intelligence
taxonomy
germplasm bank
description Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/sa/article/view/130943
10.1590/1678-992x-2015-0451
url https://www.revistas.usp.br/sa/article/view/130943
identifier_str_mv 10.1590/1678-992x-2015-0451
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/130943/127402
dc.rights.driver.fl_str_mv Copyright (c) 2017 Scientia Agricola
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Scientia Agricola
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 74 n. 3 (2017); 203-207
Scientia Agricola; Vol. 74 Núm. 3 (2017); 203-207
Scientia Agricola; Vol. 74 No. 3 (2017); 203-207
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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