Model-assisted phenotyping by digital images in papaya breeding program

Detalhes bibliográficos
Autor(a) principal: Cortes, Diego Fernando Marmolejo
Data de Publicação: 2017
Outros Autores: Catarina, Renato Santa, Barros, Gislanne Brito de Araújo, Arêdes, Fernanda Abreu Santana, Silveira, Silvaldo Felipe da, Ferreguetti, Geraldo Antônio, Ramos, Helaine Christine Cancela, Viana, Alexandre Pio, Pereira, Messias Gonzaga
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/132797
Resumo: Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the ‘THB’ variety and ‘UENF/Caliman-01’ hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In ‘THB’ fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.
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spelling Model-assisted phenotyping by digital images in papaya breeding programCarica papayaphenomicsdigital image processingManual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the ‘THB’ variety and ‘UENF/Caliman-01’ hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In ‘THB’ fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/13279710.1590/1678-992x-2016-0134Scientia Agricola; v. 74 n. 4 (2017); 294-302Scientia Agricola; Vol. 74 Núm. 4 (2017); 294-302Scientia Agricola; Vol. 74 No. 4 (2017); 294-3021678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/132797/128822Copyright (c) 2017 Scientia Agricolainfo:eu-repo/semantics/openAccessCortes, Diego Fernando MarmolejoCatarina, Renato SantaBarros, Gislanne Brito de AraújoArêdes, Fernanda Abreu SantanaSilveira, Silvaldo Felipe daFerreguetti, Geraldo AntônioRamos, Helaine Christine CancelaViana, Alexandre PioPereira, Messias Gonzaga2017-06-12T11:35:11Zoai:revistas.usp.br:article/132797Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2017-06-12T11:35:11Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Model-assisted phenotyping by digital images in papaya breeding program
title Model-assisted phenotyping by digital images in papaya breeding program
spellingShingle Model-assisted phenotyping by digital images in papaya breeding program
Cortes, Diego Fernando Marmolejo
Carica papaya
phenomics
digital image processing
title_short Model-assisted phenotyping by digital images in papaya breeding program
title_full Model-assisted phenotyping by digital images in papaya breeding program
title_fullStr Model-assisted phenotyping by digital images in papaya breeding program
title_full_unstemmed Model-assisted phenotyping by digital images in papaya breeding program
title_sort Model-assisted phenotyping by digital images in papaya breeding program
author Cortes, Diego Fernando Marmolejo
author_facet Cortes, Diego Fernando Marmolejo
Catarina, Renato Santa
Barros, Gislanne Brito de Araújo
Arêdes, Fernanda Abreu Santana
Silveira, Silvaldo Felipe da
Ferreguetti, Geraldo Antônio
Ramos, Helaine Christine Cancela
Viana, Alexandre Pio
Pereira, Messias Gonzaga
author_role author
author2 Catarina, Renato Santa
Barros, Gislanne Brito de Araújo
Arêdes, Fernanda Abreu Santana
Silveira, Silvaldo Felipe da
Ferreguetti, Geraldo Antônio
Ramos, Helaine Christine Cancela
Viana, Alexandre Pio
Pereira, Messias Gonzaga
author2_role author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Cortes, Diego Fernando Marmolejo
Catarina, Renato Santa
Barros, Gislanne Brito de Araújo
Arêdes, Fernanda Abreu Santana
Silveira, Silvaldo Felipe da
Ferreguetti, Geraldo Antônio
Ramos, Helaine Christine Cancela
Viana, Alexandre Pio
Pereira, Messias Gonzaga
dc.subject.por.fl_str_mv Carica papaya
phenomics
digital image processing
topic Carica papaya
phenomics
digital image processing
description Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the ‘THB’ variety and ‘UENF/Caliman-01’ hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In ‘THB’ fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.
publishDate 2017
dc.date.none.fl_str_mv 2017-08-01
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/132797
10.1590/1678-992x-2016-0134
url https://www.revistas.usp.br/sa/article/view/132797
identifier_str_mv 10.1590/1678-992x-2016-0134
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/132797/128822
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. 4 (2017); 294-302
Scientia Agricola; Vol. 74 Núm. 4 (2017); 294-302
Scientia Agricola; Vol. 74 No. 4 (2017); 294-302
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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