Automatic recognition of quarantine citrus diseases

Autores
Stegmayer, Georgina; Milone, Diego Humberto; Garran, Sergio; Burdyn, Lourdes
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Citrus exports to foreign markets are severely limited today by fruit diseases. Some of them, like citrus canker, black spot and scab, are quarantine for the markets. For this reason, it is important to perform strict controls before fruits are exported to avoid the inclusion of citrus affected by them. Nowadays, technical decisions are based on visual diagnosis of human experts, highly dependent on the degree of individual skills. This work presents a model capable of automatic recognize the quarantine diseases. It is based on the combination of a feature selection method and a classifier that has been trained on quarantine illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were evaluated. Experimental work was performed on 212 samples of mandarins from a Nova cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine samples of over 83% for all classes, even when using a small subset (14) of all the available features (90). The results obtained show that the proposed method can be suitable for helping the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits.
Fil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Garran, Sergio. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Burdyn, Lourdes. Instituto Nacional de Tecnología Agropecuaria; Argentina
Materia
Pattern Recognition
Multiclass Classification
Neural Networks
Citrus Diseases
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14701
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Automatic recognition of quarantine citrus diseasesStegmayer, GeorginaMilone, Diego HumbertoGarran, SergioBurdyn, LourdesPattern RecognitionMulticlass ClassificationNeural NetworksCitrus Diseaseshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Citrus exports to foreign markets are severely limited today by fruit diseases. Some of them, like citrus canker, black spot and scab, are quarantine for the markets. For this reason, it is important to perform strict controls before fruits are exported to avoid the inclusion of citrus affected by them. Nowadays, technical decisions are based on visual diagnosis of human experts, highly dependent on the degree of individual skills. This work presents a model capable of automatic recognize the quarantine diseases. It is based on the combination of a feature selection method and a classifier that has been trained on quarantine illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were evaluated. Experimental work was performed on 212 samples of mandarins from a Nova cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine samples of over 83% for all classes, even when using a small subset (14) of all the available features (90). The results obtained show that the proposed method can be suitable for helping the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits.Fil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Garran, Sergio. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Burdyn, Lourdes. Instituto Nacional de Tecnología Agropecuaria; ArgentinaElsevier2013-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14701Stegmayer, Georgina; Milone, Diego Humberto; Garran, Sergio; Burdyn, Lourdes; Automatic recognition of quarantine citrus diseases; Elsevier; Expert Systems With Applications; 40; 9; 7-2013; 3512-35170957-4174enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2012.12.059info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417412013000info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:55:27Zoai:ri.conicet.gov.ar:11336/14701instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:55:27.9CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic recognition of quarantine citrus diseases
title Automatic recognition of quarantine citrus diseases
spellingShingle Automatic recognition of quarantine citrus diseases
Stegmayer, Georgina
Pattern Recognition
Multiclass Classification
Neural Networks
Citrus Diseases
title_short Automatic recognition of quarantine citrus diseases
title_full Automatic recognition of quarantine citrus diseases
title_fullStr Automatic recognition of quarantine citrus diseases
title_full_unstemmed Automatic recognition of quarantine citrus diseases
title_sort Automatic recognition of quarantine citrus diseases
dc.creator.none.fl_str_mv Stegmayer, Georgina
Milone, Diego Humberto
Garran, Sergio
Burdyn, Lourdes
author Stegmayer, Georgina
author_facet Stegmayer, Georgina
Milone, Diego Humberto
Garran, Sergio
Burdyn, Lourdes
author_role author
author2 Milone, Diego Humberto
Garran, Sergio
Burdyn, Lourdes
author2_role author
author
author
dc.subject.none.fl_str_mv Pattern Recognition
Multiclass Classification
Neural Networks
Citrus Diseases
topic Pattern Recognition
Multiclass Classification
Neural Networks
Citrus Diseases
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Citrus exports to foreign markets are severely limited today by fruit diseases. Some of them, like citrus canker, black spot and scab, are quarantine for the markets. For this reason, it is important to perform strict controls before fruits are exported to avoid the inclusion of citrus affected by them. Nowadays, technical decisions are based on visual diagnosis of human experts, highly dependent on the degree of individual skills. This work presents a model capable of automatic recognize the quarantine diseases. It is based on the combination of a feature selection method and a classifier that has been trained on quarantine illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were evaluated. Experimental work was performed on 212 samples of mandarins from a Nova cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine samples of over 83% for all classes, even when using a small subset (14) of all the available features (90). The results obtained show that the proposed method can be suitable for helping the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits.
Fil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Garran, Sergio. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Burdyn, Lourdes. Instituto Nacional de Tecnología Agropecuaria; Argentina
description Citrus exports to foreign markets are severely limited today by fruit diseases. Some of them, like citrus canker, black spot and scab, are quarantine for the markets. For this reason, it is important to perform strict controls before fruits are exported to avoid the inclusion of citrus affected by them. Nowadays, technical decisions are based on visual diagnosis of human experts, highly dependent on the degree of individual skills. This work presents a model capable of automatic recognize the quarantine diseases. It is based on the combination of a feature selection method and a classifier that has been trained on quarantine illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were evaluated. Experimental work was performed on 212 samples of mandarins from a Nova cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine samples of over 83% for all classes, even when using a small subset (14) of all the available features (90). The results obtained show that the proposed method can be suitable for helping the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits.
publishDate 2013
dc.date.none.fl_str_mv 2013-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/14701
Stegmayer, Georgina; Milone, Diego Humberto; Garran, Sergio; Burdyn, Lourdes; Automatic recognition of quarantine citrus diseases; Elsevier; Expert Systems With Applications; 40; 9; 7-2013; 3512-3517
0957-4174
url http://hdl.handle.net/11336/14701
identifier_str_mv Stegmayer, Georgina; Milone, Diego Humberto; Garran, Sergio; Burdyn, Lourdes; Automatic recognition of quarantine citrus diseases; Elsevier; Expert Systems With Applications; 40; 9; 7-2013; 3512-3517
0957-4174
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2012.12.059
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417412013000
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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