A novel fuzzy approach to identify the phenotypic adaptability of common bean lines
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/59854 |
Resumo: | The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method. |
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Acta Scientiarum. Agronomy (Online) |
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A novel fuzzy approach to identify the phenotypic adaptability of common bean linesA novel fuzzy approach to identify the phenotypic adaptability of common bean linesartificial intelligence; fuzzy logic; plant breeding.artificial intelligence; fuzzy logic; plant breeding.The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method.The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method.Universidade Estadual de Maringá2023-03-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/5985410.4025/actasciagron.v45i1.59854Acta Scientiarum. Agronomy; Vol 45 (2023): Publicação contínua; e59854Acta Scientiarum. Agronomy; v. 45 (2023): Publicação contínua; e598541807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/59854/751375155618Copyright (c) 2023 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Carneiro, Vinícius Quintão Mencalha, Jussara Sant’anna, Isabela de CastroSilva, Gabi Nunes Miguel, Júlio Augusto de Castro Carneiro, Pedro Crescêncio Souza Nascimento, MoysésCruz, Cosme Damião 2023-04-24T18:25:48Zoai:periodicos.uem.br/ojs:article/59854Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2023-04-24T18:25:48Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
title |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
spellingShingle |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines Carneiro, Vinícius Quintão artificial intelligence; fuzzy logic; plant breeding. artificial intelligence; fuzzy logic; plant breeding. |
title_short |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
title_full |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
title_fullStr |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
title_full_unstemmed |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
title_sort |
A novel fuzzy approach to identify the phenotypic adaptability of common bean lines |
author |
Carneiro, Vinícius Quintão |
author_facet |
Carneiro, Vinícius Quintão Mencalha, Jussara Sant’anna, Isabela de Castro Silva, Gabi Nunes Miguel, Júlio Augusto de Castro Carneiro, Pedro Crescêncio Souza Nascimento, Moysés Cruz, Cosme Damião |
author_role |
author |
author2 |
Mencalha, Jussara Sant’anna, Isabela de Castro Silva, Gabi Nunes Miguel, Júlio Augusto de Castro Carneiro, Pedro Crescêncio Souza Nascimento, Moysés Cruz, Cosme Damião |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Carneiro, Vinícius Quintão Mencalha, Jussara Sant’anna, Isabela de Castro Silva, Gabi Nunes Miguel, Júlio Augusto de Castro Carneiro, Pedro Crescêncio Souza Nascimento, Moysés Cruz, Cosme Damião |
dc.subject.por.fl_str_mv |
artificial intelligence; fuzzy logic; plant breeding. artificial intelligence; fuzzy logic; plant breeding. |
topic |
artificial intelligence; fuzzy logic; plant breeding. artificial intelligence; fuzzy logic; plant breeding. |
description |
The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03-22 |
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 |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/59854 10.4025/actasciagron.v45i1.59854 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/59854 |
identifier_str_mv |
10.4025/actasciagron.v45i1.59854 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/59854/751375155618 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 45 (2023): Publicação contínua; e59854 Acta Scientiarum. Agronomy; v. 45 (2023): Publicação contínua; e59854 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
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1799305901230784512 |