Automatic classification of physical defects in green coffee beans using CGLCM and SVM

Rayner H.Montes Condori; Juan H.Chuctaya Humari; Christian E. Portugal-Zambrano; Juan C. Gutiérrez-Cáceres; César A. Beltrán-Castañón

doi:10.1109/CLEI.2014.6965169

Automatic classification of physical defects in green coffee beans using CGLCM and SVM

Rayner H.Montes Condori, Juan H.Chuctaya Humari, Christian E. Portugal-Zambrano, Juan C. Gutiérrez-Cáceres, César A. Beltrán-Castañón

Universidad Nacional de San Agustín de Arequipa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.

Original language	English
Title of host publication	Proceedings of the 2014 Latin American Computing Conference, CLEI 2014
Editors	Pablo Ezzatti, Andrea Delgado
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479961306
DOIs	https://doi.org/10.1109/CLEI.2014.6965169
State	Published - 21 Nov 2014
Event	2014 40th Latin American Computing Conference, CLEI 2014 - Montevideo, Uruguay Duration: 15 Sep 2014 → 19 Sep 2014

Publication series

Name	Proceedings of the 2014 Latin American Computing Conference, CLEI 2014

Conference

Conference	2014 40th Latin American Computing Conference, CLEI 2014
Country/Territory	Uruguay
City	Montevideo
Period	15/09/14 → 19/09/14

Keywords

coffee bean
computer vision
feature extraction
segmentation

Access to Document

10.1109/CLEI.2014.6965169

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Condori, R. H. M., Humari, J. H. C., Portugal-Zambrano, C. E., Gutiérrez-Cáceres, J. C., & Beltrán-Castañón, C. A. (2014). Automatic classification of physical defects in green coffee beans using CGLCM and SVM. In P. Ezzatti, & A. Delgado (Eds.), Proceedings of the 2014 Latin American Computing Conference, CLEI 2014 Article 6965169 (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLEI.2014.6965169

Condori, Rayner H.Montes ; Humari, Juan H.Chuctaya ; Portugal-Zambrano, Christian E. et al. / Automatic classification of physical defects in green coffee beans using CGLCM and SVM. Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. editor / Pablo Ezzatti ; Andrea Delgado. Institute of Electrical and Electronics Engineers Inc., 2014. (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014).

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title = "Automatic classification of physical defects in green coffee beans using CGLCM and SVM",

abstract = "This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.",

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note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 40th Latin American Computing Conference, CLEI 2014 ; Conference date: 15-09-2014 Through 19-09-2014",

year = "2014",

month = nov,

day = "21",

doi = "10.1109/CLEI.2014.6965169",

language = "English",

series = "Proceedings of the 2014 Latin American Computing Conference, CLEI 2014",

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Condori, RHM, Humari, JHC, Portugal-Zambrano, CE, Gutiérrez-Cáceres, JC & Beltrán-Castañón, CA 2014, Automatic classification of physical defects in green coffee beans using CGLCM and SVM. in P Ezzatti & A Delgado (eds), Proceedings of the 2014 Latin American Computing Conference, CLEI 2014., 6965169, Proceedings of the 2014 Latin American Computing Conference, CLEI 2014, Institute of Electrical and Electronics Engineers Inc., 2014 40th Latin American Computing Conference, CLEI 2014, Montevideo, Uruguay, 15/09/14. https://doi.org/10.1109/CLEI.2014.6965169

Automatic classification of physical defects in green coffee beans using CGLCM and SVM. / Condori, Rayner H.Montes; Humari, Juan H.Chuctaya; Portugal-Zambrano, Christian E. et al.
Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. ed. / Pablo Ezzatti; Andrea Delgado. Institute of Electrical and Electronics Engineers Inc., 2014. 6965169 (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Condori, Rayner H.Montes

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AU - Beltrán-Castañón, César A.

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N2 - This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.

AB - This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.

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KW - computer vision

KW - feature extraction

KW - segmentation

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Condori RHM, Humari JHC, Portugal-Zambrano CE, Gutiérrez-Cáceres JC, Beltrán-Castañón CA. Automatic classification of physical defects in green coffee beans using CGLCM and SVM. In Ezzatti P, Delgado A, editors, Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6965169. (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014). doi: 10.1109/CLEI.2014.6965169

Automatic classification of physical defects in green coffee beans using CGLCM and SVM

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