Feasibility of computational vision in the genetic improvement of sweet potato root production
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | https://doi.org/10.1590/s0102-0536-20220405 http://hdl.handle.net/1843/59821 https://orcid.org/0000-0002-8161-8130 https://orcid.org/0000-0001-7854-8111 https://orcid.org/0000-0001-6756-7835 https://orcid.org/0000-0002-1614-3239 https://orcid.org/0000-0002-2592-0677 https://orcid.org/0000-0002-0367-5607 https://orcid.org/0000-0001-8958-9808 https://orcid.org/0000-0003-2029-562X https://orcid.org/0000-0001-5196-0851 |
Resumo: | The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process. |
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2023-10-20T20:03:50Z2023-10-20T20:03:50Z2022404378383https://doi.org/10.1590/s0102-0536-202204051806-9991http://hdl.handle.net/1843/59821https://orcid.org/0000-0002-8161-8130https://orcid.org/0000-0001-7854-8111https://orcid.org/0000-0001-6756-7835https://orcid.org/0000-0002-1614-3239https://orcid.org/0000-0002-2592-0677https://orcid.org/0000-0002-0367-5607https://orcid.org/0000-0001-8958-9808https://orcid.org/0000-0003-2029-562Xhttps://orcid.org/0000-0001-5196-0851The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process.O melhoramento da batata-doce é um trabalho oneroso em decorrência do grande número de características analisadas para a seleção dos melhores genótipos, fazendo-se necessária a adoção de novas tecnologias, como o uso de imagens, associadas ao processo de fenotipagem. Objetivou-se desenvolver uma metodologia para a fenotipagem da produção de raízes para o melhoramento genético de progênies de meios irmãos de batata-doce por meio da análise computacional de imagens e comparar seu desempenho com a metodologia tradicional de avaliação. Foram avaliadas 16 famílias de meios irmãos de batata-doce em delineamento de blocos casualizados com 4 repetições. Avaliou-se a nível de plantas o peso por raiz. As imagens foram adquiridas em um “estúdio” feito com mdf com uma câmera digital modelo Canon PowerShotSX400 IS, sob iluminação artificial. As avaliações foram realizadas no software R, onde para a predição do peso das raízes (em gramas) foi ajustado um modelo de regressão polinomial de segundo grau e foram obtidos os valores genéticos e ganhos esperados. Foi possível predizer o peso das raízes a nível de plantas e de parcelas, obtendo altos coeficientes de determinação entre o peso predito e observado. A visão computacional permitiu a predição do peso das raízes, mantendo o ranqueamento dos genótipos e consequentemente a similaridade entre os ganhos esperados com a seleção. Assim, o uso de imagens é uma ferramenta eficiente para programas de melhoramento genético da batata-doce, auxiliando no processo de fenotipagem da cultura.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorengUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASHorticultura BrasileiraBatata-doceGenética vegetalMelhoramento genéticoAnálise de imagemFeasibility of computational vision in the genetic improvement of sweet potato root productionViabilidade da visão computacional no melhoramento genético na produção de raízes de batata-doceinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.scielo.br/j/hb/a/Gj6QyQkqQtyfnXKDXK3MVRv/?format=pdf&lang=enAna Clara Gonçalves FernandesNermy Ribeiro ValadaresClóvis Henrique Oliveira RodriguesRayane Aguiar AlvesLis Lorena Melúcio GuedesJailson Ramos MagalhãesRafael Bolina da SilvaLuan Souza de Paula GomesAlcinei Místico Azevedoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/59821/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALFeasibility of computational vision in the genetic improvement of sweet potato root production.pdfFeasibility of computational vision in the genetic improvement of sweet potato root production.pdfapplication/pdf957757https://repositorio.ufmg.br/bitstream/1843/59821/2/Feasibility%20of%20computational%20vision%20in%20the%20genetic%20improvement%20of%20sweet%20potato%20root%20production.pdff5e2c42074a15d81e5a8bdfb5568f37cMD521843/598212023-10-23 16:51:59.565oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-10-23T19:51:59Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
dc.title.alternative.pt_BR.fl_str_mv |
Viabilidade da visão computacional no melhoramento genético na produção de raízes de batata-doce |
title |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
spellingShingle |
Feasibility of computational vision in the genetic improvement of sweet potato root production Ana Clara Gonçalves Fernandes Batata-doce Genética vegetal Melhoramento genético Análise de imagem |
title_short |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
title_full |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
title_fullStr |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
title_full_unstemmed |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
title_sort |
Feasibility of computational vision in the genetic improvement of sweet potato root production |
author |
Ana Clara Gonçalves Fernandes |
author_facet |
Ana Clara Gonçalves Fernandes Nermy Ribeiro Valadares Clóvis Henrique Oliveira Rodrigues Rayane Aguiar Alves Lis Lorena Melúcio Guedes Jailson Ramos Magalhães Rafael Bolina da Silva Luan Souza de Paula Gomes Alcinei Místico Azevedo |
author_role |
author |
author2 |
Nermy Ribeiro Valadares Clóvis Henrique Oliveira Rodrigues Rayane Aguiar Alves Lis Lorena Melúcio Guedes Jailson Ramos Magalhães Rafael Bolina da Silva Luan Souza de Paula Gomes Alcinei Místico Azevedo |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Ana Clara Gonçalves Fernandes Nermy Ribeiro Valadares Clóvis Henrique Oliveira Rodrigues Rayane Aguiar Alves Lis Lorena Melúcio Guedes Jailson Ramos Magalhães Rafael Bolina da Silva Luan Souza de Paula Gomes Alcinei Místico Azevedo |
dc.subject.other.pt_BR.fl_str_mv |
Batata-doce Genética vegetal Melhoramento genético Análise de imagem |
topic |
Batata-doce Genética vegetal Melhoramento genético Análise de imagem |
description |
The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022 |
dc.date.accessioned.fl_str_mv |
2023-10-20T20:03:50Z |
dc.date.available.fl_str_mv |
2023-10-20T20:03:50Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/59821 |
dc.identifier.doi.pt_BR.fl_str_mv |
https://doi.org/10.1590/s0102-0536-20220405 |
dc.identifier.issn.pt_BR.fl_str_mv |
1806-9991 |
dc.identifier.orcid.pt_BR.fl_str_mv |
https://orcid.org/0000-0002-8161-8130 https://orcid.org/0000-0001-7854-8111 https://orcid.org/0000-0001-6756-7835 https://orcid.org/0000-0002-1614-3239 https://orcid.org/0000-0002-2592-0677 https://orcid.org/0000-0002-0367-5607 https://orcid.org/0000-0001-8958-9808 https://orcid.org/0000-0003-2029-562X https://orcid.org/0000-0001-5196-0851 |
url |
https://doi.org/10.1590/s0102-0536-20220405 http://hdl.handle.net/1843/59821 https://orcid.org/0000-0002-8161-8130 https://orcid.org/0000-0001-7854-8111 https://orcid.org/0000-0001-6756-7835 https://orcid.org/0000-0002-1614-3239 https://orcid.org/0000-0002-2592-0677 https://orcid.org/0000-0002-0367-5607 https://orcid.org/0000-0001-8958-9808 https://orcid.org/0000-0003-2029-562X https://orcid.org/0000-0001-5196-0851 |
identifier_str_mv |
1806-9991 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.none.fl_str_mv |
Horticultura Brasileira |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
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reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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UFMG |
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Repositório Institucional da UFMG |
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