Weed recognition by SVM texture feature classification in outdoor vegetable crops images

This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feat...

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Autores:
Pulido Rojas, Camilo
Solaque Guzmán, Leonardo
Velasco Toledo, Nelson
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/67585
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/67585
http://bdigital.unal.edu.co/68614/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
Weed recognition
support vectors
co-occurrence matrix
PCA
Reconocimiento de maleza
vectores de soporte
matrices de co-ocurrencia
PCA
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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oai_identifier_str oai:repositorio.unal.edu.co:unal/67585
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network_name_str Universidad Nacional de Colombia
repository_id_str
spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pulido Rojas, Camilo9616a771-9542-4337-8911-1ffc040fc7ac300Solaque Guzmán, Leonardobcca3ee6-1006-42c5-99e0-a52307e463f7300Velasco Toledo, Nelson37aeaedc-06d5-4eea-9825-34762a9797db3002019-07-03T04:36:30Z2019-07-03T04:36:30Z2017-01-01ISSN: 2248-8723https://repositorio.unal.edu.co/handle/unal/67585http://bdigital.unal.edu.co/68614/This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feature space is the result of principal component analysis (PCA) for 10 texture measurements calculated from gray level co-occurrence matrices (GLCM). The results indicate that classifier performance is above 90%, validated with specificity, sensitivity and precision calculations.El presente trabajo expone un sistema de clasificación de maleza y hortalizas a partir de imágenes exteriores de cultivos. El clasificador está basado en la teoría de las máquinas de vectores de soporte (Support Vector Machine SVM) con su extensión para el caso no lineal, haciendo uso de la función de base radial (RBF) y optimizando su parámetro de escala σ para suavizar la región de decisión. El espacio de características es el resultado del análisis por componentes principales (PCA) de 10 medidas de textura calculadas a partir de matrices de co-ocurrencia en niveles de gris (GLCM). Los resultados indican un rendimiento del clasificador por encima del 90% calculando los índices de especificidad, sensibilidad y precisión.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ingenieríahttps://revistas.unal.edu.co/index.php/ingeinv/article/view/54703Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e InvestigaciónIngeniería e InvestigaciónPulido Rojas, Camilo and Solaque Guzmán, Leonardo and Velasco Toledo, Nelson (2017) Weed recognition by SVM texture feature classification in outdoor vegetable crops images. Ingeniería e Investigación, 37 (1). pp. 68-74. ISSN 2248-872362 Ingeniería y operaciones afines / EngineeringWeed recognitionsupport vectorsco-occurrence matrixPCAReconocimiento de malezavectores de soportematrices de co-ocurrenciaPCAWeed recognition by SVM texture feature classification in outdoor vegetable crops imagesArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL54703-325927-1-PB.pdfapplication/pdf1909348https://repositorio.unal.edu.co/bitstream/unal/67585/1/54703-325927-1-PB.pdf93a1c374684d44866d02026be3e81531MD51THUMBNAIL54703-325927-1-PB.pdf.jpg54703-325927-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg8300https://repositorio.unal.edu.co/bitstream/unal/67585/2/54703-325927-1-PB.pdf.jpg6c4fb1529dc1de90fb618b2199aea1b9MD52unal/67585oai:repositorio.unal.edu.co:unal/675852023-05-30 23:03:15.398Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Weed recognition by SVM texture feature classification in outdoor vegetable crops images
title Weed recognition by SVM texture feature classification in outdoor vegetable crops images
spellingShingle Weed recognition by SVM texture feature classification in outdoor vegetable crops images
62 Ingeniería y operaciones afines / Engineering
Weed recognition
support vectors
co-occurrence matrix
PCA
Reconocimiento de maleza
vectores de soporte
matrices de co-ocurrencia
PCA
title_short Weed recognition by SVM texture feature classification in outdoor vegetable crops images
title_full Weed recognition by SVM texture feature classification in outdoor vegetable crops images
title_fullStr Weed recognition by SVM texture feature classification in outdoor vegetable crops images
title_full_unstemmed Weed recognition by SVM texture feature classification in outdoor vegetable crops images
title_sort Weed recognition by SVM texture feature classification in outdoor vegetable crops images
dc.creator.fl_str_mv Pulido Rojas, Camilo
Solaque Guzmán, Leonardo
Velasco Toledo, Nelson
dc.contributor.author.spa.fl_str_mv Pulido Rojas, Camilo
Solaque Guzmán, Leonardo
Velasco Toledo, Nelson
dc.subject.ddc.spa.fl_str_mv 62 Ingeniería y operaciones afines / Engineering
topic 62 Ingeniería y operaciones afines / Engineering
Weed recognition
support vectors
co-occurrence matrix
PCA
Reconocimiento de maleza
vectores de soporte
matrices de co-ocurrencia
PCA
dc.subject.proposal.spa.fl_str_mv Weed recognition
support vectors
co-occurrence matrix
PCA
Reconocimiento de maleza
vectores de soporte
matrices de co-ocurrencia
PCA
description This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feature space is the result of principal component analysis (PCA) for 10 texture measurements calculated from gray level co-occurrence matrices (GLCM). The results indicate that classifier performance is above 90%, validated with specificity, sensitivity and precision calculations.
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017-01-01
dc.date.accessioned.spa.fl_str_mv 2019-07-03T04:36:30Z
dc.date.available.spa.fl_str_mv 2019-07-03T04:36:30Z
dc.type.spa.fl_str_mv Artículo de revista
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status_str publishedVersion
dc.identifier.issn.spa.fl_str_mv ISSN: 2248-8723
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/67585
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/68614/
identifier_str_mv ISSN: 2248-8723
url https://repositorio.unal.edu.co/handle/unal/67585
http://bdigital.unal.edu.co/68614/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/ingeinv/article/view/54703
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación
Ingeniería e Investigación
dc.relation.references.spa.fl_str_mv Pulido Rojas, Camilo and Solaque Guzmán, Leonardo and Velasco Toledo, Nelson (2017) Weed recognition by SVM texture feature classification in outdoor vegetable crops images. Ingeniería e Investigación, 37 (1). pp. 68-74. ISSN 2248-8723
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ingeniería
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/67585/1/54703-325927-1-PB.pdf
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
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