Automated Counting of Meloidogyne javanica Galls in Vegetable Roots
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
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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Summa phytopathologica (Online) |
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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 |
_version_ |
1754193419691884544 |