Automated Counting of Meloidogyne javanica Galls in Vegetable Roots

Detalhes bibliográficos
Autor(a) principal: Abe,Vinícius Hicaro Frederico
Data de Publicação: 2019
Outros Autores: Miamoto,Angélica, Felinto,Alan Salvany, Peres,Frederico Oldenburg, Dias-Arieira,Cláudia Regina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Summa phytopathologica (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-54052019000400381
Resumo: ABSTRACT Root-knot nematodes, genus Meloidogyne spp., are among the most destructive parasites of cultivated plants. The characteristic symptom of this disease is gall formation in the root system. Genetic resistance is one of the most efficient and economic methods of minimal environmental impact to control this endoparasite, and gall index has been used to select resistant varieties. However, this method is based on visual assessment of galls and is therefore a time-consuming and error-prone technique. Thus, this study aims to develop an automated computational method for Meloidogyne javanica gall counting. The proposed method was composed of five steps: visual counting of galls, image acquisition by a scanner, optimization of parameters based on the image group and image counting. Lettuce root showed the best results, with 1% mean relative error, while tomato root had the worst result, showing 32% mean relative error. The mean relative error for all tested roots was 13%.
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spelling Automated Counting of Meloidogyne javanica Galls in Vegetable RootsNematodesCircular CorrelationComputer VisionGenetic AlgorithmABSTRACT Root-knot nematodes, genus Meloidogyne spp., are among the most destructive parasites of cultivated plants. The characteristic symptom of this disease is gall formation in the root system. Genetic resistance is one of the most efficient and economic methods of minimal environmental impact to control this endoparasite, and gall index has been used to select resistant varieties. However, this method is based on visual assessment of galls and is therefore a time-consuming and error-prone technique. Thus, this study aims to develop an automated computational method for Meloidogyne javanica gall counting. The proposed method was composed of five steps: visual counting of galls, image acquisition by a scanner, optimization of parameters based on the image group and image counting. Lettuce root showed the best results, with 1% mean relative error, while tomato root had the worst result, showing 32% mean relative error. The mean relative error for all tested roots was 13%.Grupo Paulista de Fitopatologia2019-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-54052019000400381Summa Phytopathologica v.45 n.4 2019reponame:Summa phytopathologica (Online)instname:Grupo Paulista de Fitopatologiainstacron:GPF10.1590/0100-5405/193307info:eu-repo/semantics/openAccessAbe,Vinícius Hicaro FredericoMiamoto,AngélicaFelinto,Alan SalvanyPeres,Frederico OldenburgDias-Arieira,Cláudia Reginaeng2020-01-14T00:00:00Zoai:scielo:S0100-54052019000400381Revistahttp://www.scielo.br/sphttps://old.scielo.br/oai/scielo-oai.phpsumma@fca.unesp.br1980-54540100-5405opendoar:2020-01-14T00:00Summa phytopathologica (Online) - Grupo Paulista de Fitopatologiafalse
dc.title.none.fl_str_mv Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
title Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
spellingShingle Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
Abe,Vinícius Hicaro Frederico
Nematodes
Circular Correlation
Computer Vision
Genetic Algorithm
title_short Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
title_full Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
title_fullStr Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
title_full_unstemmed Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
title_sort Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
author Abe,Vinícius Hicaro Frederico
author_facet Abe,Vinícius Hicaro Frederico
Miamoto,Angélica
Felinto,Alan Salvany
Peres,Frederico Oldenburg
Dias-Arieira,Cláudia Regina
author_role author
author2 Miamoto,Angélica
Felinto,Alan Salvany
Peres,Frederico Oldenburg
Dias-Arieira,Cláudia Regina
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Abe,Vinícius Hicaro Frederico
Miamoto,Angélica
Felinto,Alan Salvany
Peres,Frederico Oldenburg
Dias-Arieira,Cláudia Regina
dc.subject.por.fl_str_mv Nematodes
Circular Correlation
Computer Vision
Genetic Algorithm
topic Nematodes
Circular Correlation
Computer Vision
Genetic Algorithm
description ABSTRACT Root-knot nematodes, genus Meloidogyne spp., are among the most destructive parasites of cultivated plants. The characteristic symptom of this disease is gall formation in the root system. Genetic resistance is one of the most efficient and economic methods of minimal environmental impact to control this endoparasite, and gall index has been used to select resistant varieties. However, this method is based on visual assessment of galls and is therefore a time-consuming and error-prone technique. Thus, this study aims to develop an automated computational method for Meloidogyne javanica gall counting. The proposed method was composed of five steps: visual counting of galls, image acquisition by a scanner, optimization of parameters based on the image group and image counting. Lettuce root showed the best results, with 1% mean relative error, while tomato root had the worst result, showing 32% mean relative error. The mean relative error for all tested roots was 13%.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-01
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=S0100-54052019000400381
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-54052019000400381
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0100-5405/193307
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 Grupo Paulista de Fitopatologia
publisher.none.fl_str_mv Grupo Paulista de Fitopatologia
dc.source.none.fl_str_mv Summa Phytopathologica v.45 n.4 2019
reponame:Summa phytopathologica (Online)
instname:Grupo Paulista de Fitopatologia
instacron:GPF
instname_str Grupo Paulista de Fitopatologia
instacron_str GPF
institution GPF
reponame_str Summa phytopathologica (Online)
collection Summa phytopathologica (Online)
repository.name.fl_str_mv Summa phytopathologica (Online) - Grupo Paulista de Fitopatologia
repository.mail.fl_str_mv summa@fca.unesp.br
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