Automatic classification of legumes using leaf vein image features

Autores
Larese, Monica Graciela; Namias, Rafael; Craviotto, Roque Mario; Arango, Miriam Raquel; Gallo, Carina del Valle; Granitto, Pablo Miguel
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.
Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Craviotto, Roque Mario. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina
Fil: Arango, Miriam Raquel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina
Fil: Gallo, Carina del Valle. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Materia
LEAF VEIN ANALYSIS
LEAF VEIN FEATURES
LEAF VEIN IMAGES
LEGUME CLASSIFICATION
UNCONSTRAINED HIT-OR-MISS TRANSFORM
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:11336/3198
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automatic classification of legumes using leaf vein image featuresLarese, Monica GracielaNamias, RafaelCraviotto, Roque MarioArango, Miriam RaquelGallo, Carina del ValleGranitto, Pablo MiguelLEAF VEIN ANALYSISLEAF VEIN FEATURESLEAF VEIN IMAGESLEGUME CLASSIFICATIONUNCONSTRAINED HIT-OR-MISS TRANSFORMhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Craviotto, Roque Mario. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Arango, Miriam Raquel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Gallo, Carina del Valle. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaElsevier2013-06-21info: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/11336/3198Larese, Monica Graciela; Namias, Rafael; Craviotto, Roque Mario; Arango, Miriam Raquel; Gallo, Carina del Valle; et al.; Automatic classification of legumes using leaf vein image features; Elsevier; Pattern Recognition; 47; 1; 21-6-2013; 158-1680031-3203enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0031320313002641info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patcog.2013.06.012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:03:08Zoai:ri.conicet.gov.ar:11336/3198instacron: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:34982025-10-15 15:03:09.27CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic classification of legumes using leaf vein image features
title Automatic classification of legumes using leaf vein image features
spellingShingle Automatic classification of legumes using leaf vein image features
Larese, Monica Graciela
LEAF VEIN ANALYSIS
LEAF VEIN FEATURES
LEAF VEIN IMAGES
LEGUME CLASSIFICATION
UNCONSTRAINED HIT-OR-MISS TRANSFORM
title_short Automatic classification of legumes using leaf vein image features
title_full Automatic classification of legumes using leaf vein image features
title_fullStr Automatic classification of legumes using leaf vein image features
title_full_unstemmed Automatic classification of legumes using leaf vein image features
title_sort Automatic classification of legumes using leaf vein image features
dc.creator.none.fl_str_mv Larese, Monica Graciela
Namias, Rafael
Craviotto, Roque Mario
Arango, Miriam Raquel
Gallo, Carina del Valle
Granitto, Pablo Miguel
author Larese, Monica Graciela
author_facet Larese, Monica Graciela
Namias, Rafael
Craviotto, Roque Mario
Arango, Miriam Raquel
Gallo, Carina del Valle
Granitto, Pablo Miguel
author_role author
author2 Namias, Rafael
Craviotto, Roque Mario
Arango, Miriam Raquel
Gallo, Carina del Valle
Granitto, Pablo Miguel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv LEAF VEIN ANALYSIS
LEAF VEIN FEATURES
LEAF VEIN IMAGES
LEGUME CLASSIFICATION
UNCONSTRAINED HIT-OR-MISS TRANSFORM
topic LEAF VEIN ANALYSIS
LEAF VEIN FEATURES
LEAF VEIN IMAGES
LEGUME CLASSIFICATION
UNCONSTRAINED HIT-OR-MISS TRANSFORM
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.
Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Craviotto, Roque Mario. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina
Fil: Arango, Miriam Raquel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina
Fil: Gallo, Carina del Valle. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
description In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.
publishDate 2013
dc.date.none.fl_str_mv 2013-06-21
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/11336/3198
Larese, Monica Graciela; Namias, Rafael; Craviotto, Roque Mario; Arango, Miriam Raquel; Gallo, Carina del Valle; et al.; Automatic classification of legumes using leaf vein image features; Elsevier; Pattern Recognition; 47; 1; 21-6-2013; 158-168
0031-3203
url http://hdl.handle.net/11336/3198
identifier_str_mv Larese, Monica Graciela; Namias, Rafael; Craviotto, Roque Mario; Arango, Miriam Raquel; Gallo, Carina del Valle; et al.; Automatic classification of legumes using leaf vein image features; Elsevier; Pattern Recognition; 47; 1; 21-6-2013; 158-168
0031-3203
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0031320313002641
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patcog.2013.06.012
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
_version_ 1846083173757222912
score 13.22299

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