Automatic recognition of quarantine citrus diseases

Autores
Stegmayer, Georgina; Milone, Diego Humberto; Garran, Sergio Mario; 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; 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; Argentina
Fil: Garran, Sergio Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Burdyn, Lourdes. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fuente
Expert systems with applications 40 : 3512–3517. (2013)
Materia
Citrus
Enfermedades de las Plantas
Cuarentena
Métodos de Control
Clasificación
Classification
Control Methods
Quarantine
Plant Diseases
Neural Networks
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/2784
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oai_identifier_str oai:localhost:20.500.12123/2784
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spelling Automatic recognition of quarantine citrus diseasesStegmayer, GeorginaMilone, Diego HumbertoGarran, Sergio MarioBurdyn, LourdesCitrusEnfermedades de las PlantasCuarentenaMétodos de ControlClasificaciónClassificationControl MethodsQuarantinePlant DiseasesNeural NetworksCitrus 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; 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; Argentina; ArgentinaFil: Garran, Sergio Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; ArgentinaFil: Burdyn, Lourdes. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina2018-07-12T18:47:30Z2018-07-12T18:47:30Z2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/27840957-4174https://doi.org/10.1016/j.eswa.2012.12.059Expert systems with applications 40 : 3512–3517. (2013)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-04T09:47:21Zoai:localhost:20.500.12123/2784instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-04 09:47:22.264INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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
Citrus
Enfermedades de las Plantas
Cuarentena
Métodos de Control
Clasificación
Classification
Control Methods
Quarantine
Plant Diseases
Neural Networks
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 Mario
Burdyn, Lourdes
author Stegmayer, Georgina
author_facet Stegmayer, Georgina
Milone, Diego Humberto
Garran, Sergio Mario
Burdyn, Lourdes
author_role author
author2 Milone, Diego Humberto
Garran, Sergio Mario
Burdyn, Lourdes
author2_role author
author
author
dc.subject.none.fl_str_mv Citrus
Enfermedades de las Plantas
Cuarentena
Métodos de Control
Clasificación
Classification
Control Methods
Quarantine
Plant Diseases
Neural Networks
topic Citrus
Enfermedades de las Plantas
Cuarentena
Métodos de Control
Clasificación
Classification
Control Methods
Quarantine
Plant Diseases
Neural Networks
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; 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; Argentina
Fil: Garran, Sergio Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Burdyn, Lourdes. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; 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
2018-07-12T18:47:30Z
2018-07-12T18:47:30Z
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/20.500.12123/2784
0957-4174
https://doi.org/10.1016/j.eswa.2012.12.059
url http://hdl.handle.net/20.500.12123/2784
https://doi.org/10.1016/j.eswa.2012.12.059
identifier_str_mv 0957-4174
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Expert systems with applications 40 : 3512–3517. (2013)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.623145

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