Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001200123 |
Resumo: | ABSTRACT: The methods of Annicchiarico (1992) and Cruz et al. (1989) are widely used in phenotypic adaptability and stability analyses in plant breeding. In spite of the importance of these methodologies, their parameters are difficult to interpret. The aim of this research was to develop fuzzy controllers to automate the decision-making process employed by adaptability and stability studies following the methods adopted by Annicchiarico (1992) and Cruz et al. (1989) and check their efficiency using experimental data from common bean cultivars. Fuzzy controllers have been developed based on the Mamdani inference system proposed by these two methods of adaptability and stability studies. For the first fuzzy controller parameters were considered favorable environments and the recommendation index for unfavorable environments obtained by Annicchiarico's method (1992). For the second controller the parameters considered were the general mean (β0), coefficient of regression of unfavorable environments (β1) and coefficient of favorable environments (β1i + β2i) and the coefficient of determination of the method of Cruz et al. (1989). To check the performance of these drivers yield data from field trials on 18 common bean cultivars grown in 11 environments were used. The controllers were developed from established routines in the R software and, using the inference system based on the methods proposed by Annicchiarico (1992) and Cruz et al. (1989), classified the 18 genotypes appropriately in accordance with the criteria for each method. Thus, the methods used are effective, and are prescribed for decision-making automation in yield adaptability and stability studies pertaining to recommendation of cultivars. |
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Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studiescommon beangenotype by environment interactioncrop breedingcomputational intelligenceABSTRACT: The methods of Annicchiarico (1992) and Cruz et al. (1989) are widely used in phenotypic adaptability and stability analyses in plant breeding. In spite of the importance of these methodologies, their parameters are difficult to interpret. The aim of this research was to develop fuzzy controllers to automate the decision-making process employed by adaptability and stability studies following the methods adopted by Annicchiarico (1992) and Cruz et al. (1989) and check their efficiency using experimental data from common bean cultivars. Fuzzy controllers have been developed based on the Mamdani inference system proposed by these two methods of adaptability and stability studies. For the first fuzzy controller parameters were considered favorable environments and the recommendation index for unfavorable environments obtained by Annicchiarico's method (1992). For the second controller the parameters considered were the general mean (β0), coefficient of regression of unfavorable environments (β1) and coefficient of favorable environments (β1i + β2i) and the coefficient of determination of the method of Cruz et al. (1989). To check the performance of these drivers yield data from field trials on 18 common bean cultivars grown in 11 environments were used. The controllers were developed from established routines in the R software and, using the inference system based on the methods proposed by Annicchiarico (1992) and Cruz et al. (1989), classified the 18 genotypes appropriately in accordance with the criteria for each method. Thus, the methods used are effective, and are prescribed for decision-making automation in yield adaptability and stability studies pertaining to recommendation of cultivars.Escola Superior de Agricultura "Luiz de Queiroz"2019-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001200123Scientia Agricola v.76 n.2 2019reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2017-0207info:eu-repo/semantics/openAccessCarneiro,Anna Regina TiagoSanglard,Demerson ArrudaAzevedo,Alcinei MisticoSouza,Thiago Lívio Pessoa Oliveira dePereira,Helton SantosMelo,Leonardo Cunhaeng2018-12-04T00:00:00Zoai:scielo:S0103-90162019001200123Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2018-12-04T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
title |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
spellingShingle |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies Carneiro,Anna Regina Tiago common bean genotype by environment interaction crop breeding computational intelligence |
title_short |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
title_full |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
title_fullStr |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
title_full_unstemmed |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
title_sort |
Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies |
author |
Carneiro,Anna Regina Tiago |
author_facet |
Carneiro,Anna Regina Tiago Sanglard,Demerson Arruda Azevedo,Alcinei Mistico Souza,Thiago Lívio Pessoa Oliveira de Pereira,Helton Santos Melo,Leonardo Cunha |
author_role |
author |
author2 |
Sanglard,Demerson Arruda Azevedo,Alcinei Mistico Souza,Thiago Lívio Pessoa Oliveira de Pereira,Helton Santos Melo,Leonardo Cunha |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Carneiro,Anna Regina Tiago Sanglard,Demerson Arruda Azevedo,Alcinei Mistico Souza,Thiago Lívio Pessoa Oliveira de Pereira,Helton Santos Melo,Leonardo Cunha |
dc.subject.por.fl_str_mv |
common bean genotype by environment interaction crop breeding computational intelligence |
topic |
common bean genotype by environment interaction crop breeding computational intelligence |
description |
ABSTRACT: The methods of Annicchiarico (1992) and Cruz et al. (1989) are widely used in phenotypic adaptability and stability analyses in plant breeding. In spite of the importance of these methodologies, their parameters are difficult to interpret. The aim of this research was to develop fuzzy controllers to automate the decision-making process employed by adaptability and stability studies following the methods adopted by Annicchiarico (1992) and Cruz et al. (1989) and check their efficiency using experimental data from common bean cultivars. Fuzzy controllers have been developed based on the Mamdani inference system proposed by these two methods of adaptability and stability studies. For the first fuzzy controller parameters were considered favorable environments and the recommendation index for unfavorable environments obtained by Annicchiarico's method (1992). For the second controller the parameters considered were the general mean (β0), coefficient of regression of unfavorable environments (β1) and coefficient of favorable environments (β1i + β2i) and the coefficient of determination of the method of Cruz et al. (1989). To check the performance of these drivers yield data from field trials on 18 common bean cultivars grown in 11 environments were used. The controllers were developed from established routines in the R software and, using the inference system based on the methods proposed by Annicchiarico (1992) and Cruz et al. (1989), classified the 18 genotypes appropriately in accordance with the criteria for each method. Thus, the methods used are effective, and are prescribed for decision-making automation in yield adaptability and stability studies pertaining to recommendation of cultivars. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-04-01 |
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=S0103-90162019001200123 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001200123 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-992x-2017-0207 |
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 |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.76 n.2 2019 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 |
_version_ |
1748936464806182912 |