Development of a digital image classification system to support technical assistance for Black Sigatoka detection
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista brasileira de fruticultura (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-29452021000200401 |
Resumo: | Abstract A large percentage of Colombia’s economic activity corresponds to the agricultural sector. In this sector, plantains rank second in production and planted area. However this crop is affected by different diseases, among which The Black Sigatoka stands out, caused by the fungus Mycosphaerella fijiensis. The disease highly reduces the production level of the crop and although there are prevention measures that allow reducing the incidence of the disease, there’s a lack of support for small producers in Colombia, who do not have technological tools to support the disease detection processes. This article outlines the development of a support system for the detection of black sigatoka using digital images. For this, a characterization process of the agricultural user is carried out, then, a machine learning methodology is implemented to classify the disease on a mobile device. The support system is validated through laboratory tests, field tests and the feedback from the agricultural user. |
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Development of a digital image classification system to support technical assistance for Black Sigatoka detectionBlack sigatokabananaagricultural sectormachine learningmobile applicationAbstract A large percentage of Colombia’s economic activity corresponds to the agricultural sector. In this sector, plantains rank second in production and planted area. However this crop is affected by different diseases, among which The Black Sigatoka stands out, caused by the fungus Mycosphaerella fijiensis. The disease highly reduces the production level of the crop and although there are prevention measures that allow reducing the incidence of the disease, there’s a lack of support for small producers in Colombia, who do not have technological tools to support the disease detection processes. This article outlines the development of a support system for the detection of black sigatoka using digital images. For this, a characterization process of the agricultural user is carried out, then, a machine learning methodology is implemented to classify the disease on a mobile device. The support system is validated through laboratory tests, field tests and the feedback from the agricultural user.Sociedade Brasileira de Fruticultura2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-29452021000200401Revista Brasileira de Fruticultura v.43 n.2 2021reponame:Revista brasileira de fruticultura (Online)instname:Sociedade Brasileira de Fruticultura (SBF)instacron:SBFRU10.1590/0100-29452020681info:eu-repo/semantics/openAccessEscudero,Cristian AndrésCalvo,Andrés FelipeMartinez,Arley BejaranoLópez,Ana MaríaMolina,Alexandereng2021-05-24T00:00:00Zoai:scielo:S0100-29452021000200401Revistahttp://www.scielo.br/rbfhttps://old.scielo.br/oai/scielo-oai.phprbf@fcav.unesp.br||http://rbf.org.br/1806-99670100-2945opendoar:2021-05-24T00:00Revista brasileira de fruticultura (Online) - Sociedade Brasileira de Fruticultura (SBF)false |
dc.title.none.fl_str_mv |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
title |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
spellingShingle |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection Escudero,Cristian Andrés Black sigatoka banana agricultural sector machine learning mobile application |
title_short |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
title_full |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
title_fullStr |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
title_full_unstemmed |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
title_sort |
Development of a digital image classification system to support technical assistance for Black Sigatoka detection |
author |
Escudero,Cristian Andrés |
author_facet |
Escudero,Cristian Andrés Calvo,Andrés Felipe Martinez,Arley Bejarano López,Ana María Molina,Alexander |
author_role |
author |
author2 |
Calvo,Andrés Felipe Martinez,Arley Bejarano López,Ana María Molina,Alexander |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Escudero,Cristian Andrés Calvo,Andrés Felipe Martinez,Arley Bejarano López,Ana María Molina,Alexander |
dc.subject.por.fl_str_mv |
Black sigatoka banana agricultural sector machine learning mobile application |
topic |
Black sigatoka banana agricultural sector machine learning mobile application |
description |
Abstract A large percentage of Colombia’s economic activity corresponds to the agricultural sector. In this sector, plantains rank second in production and planted area. However this crop is affected by different diseases, among which The Black Sigatoka stands out, caused by the fungus Mycosphaerella fijiensis. The disease highly reduces the production level of the crop and although there are prevention measures that allow reducing the incidence of the disease, there’s a lack of support for small producers in Colombia, who do not have technological tools to support the disease detection processes. This article outlines the development of a support system for the detection of black sigatoka using digital images. For this, a characterization process of the agricultural user is carried out, then, a machine learning methodology is implemented to classify the disease on a mobile device. The support system is validated through laboratory tests, field tests and the feedback from the agricultural user. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
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=S0100-29452021000200401 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-29452021000200401 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0100-29452020681 |
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 |
Sociedade Brasileira de Fruticultura |
publisher.none.fl_str_mv |
Sociedade Brasileira de Fruticultura |
dc.source.none.fl_str_mv |
Revista Brasileira de Fruticultura v.43 n.2 2021 reponame:Revista brasileira de fruticultura (Online) instname:Sociedade Brasileira de Fruticultura (SBF) instacron:SBFRU |
instname_str |
Sociedade Brasileira de Fruticultura (SBF) |
instacron_str |
SBFRU |
institution |
SBFRU |
reponame_str |
Revista brasileira de fruticultura (Online) |
collection |
Revista brasileira de fruticultura (Online) |
repository.name.fl_str_mv |
Revista brasileira de fruticultura (Online) - Sociedade Brasileira de Fruticultura (SBF) |
repository.mail.fl_str_mv |
rbf@fcav.unesp.br||http://rbf.org.br/ |
_version_ |
1752122496469958656 |