A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties

Producción Científica

Detalles Bibliográficos
Autores: Pourdarbani, Razieh, Sabzi, Sajad, Kalantari, Davood, Hernández Hernández, José Luis, Arribas Sánchez, Juan Ignacio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/52628
Acceso en línea:https://doi.org/10.3390/foods9020113
https://uvadoc.uva.es/handle/10324/52628
Access Level:acceso abierto
Palabra clave:Chickpeas
Garbanzos
Computer vision
Visión artificial
Image processing
Procesamiento de imágenes
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spelling A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varietiesPourdarbani, RaziehSabzi, SajadKalantari, DavoodHernández Hernández, José LuisArribas Sánchez, Juan IgnacioChickpeasGarbanzosComputer visionVisión artificialImage processingProcesamiento de imágenesProducción CientíficaSince different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP)MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/foods9020113https://uvadoc.uva.es/handle/10324/52628reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.mdpi.com/2304-8158/9/2/113info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/526282026-06-13T12:44:47Z
dc.title.none.fl_str_mv A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
title A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
spellingShingle A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
Pourdarbani, Razieh
Chickpeas
Garbanzos
Computer vision
Visión artificial
Image processing
Procesamiento de imágenes
title_short A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
title_full A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
title_fullStr A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
title_full_unstemmed A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
title_sort A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
dc.creator.none.fl_str_mv Pourdarbani, Razieh
Sabzi, Sajad
Kalantari, Davood
Hernández Hernández, José Luis
Arribas Sánchez, Juan Ignacio
author Pourdarbani, Razieh
author_facet Pourdarbani, Razieh
Sabzi, Sajad
Kalantari, Davood
Hernández Hernández, José Luis
Arribas Sánchez, Juan Ignacio
author_role author
author2 Sabzi, Sajad
Kalantari, Davood
Hernández Hernández, José Luis
Arribas Sánchez, Juan Ignacio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Chickpeas
Garbanzos
Computer vision
Visión artificial
Image processing
Procesamiento de imágenes
topic Chickpeas
Garbanzos
Computer vision
Visión artificial
Image processing
Procesamiento de imágenes
description Producción Científica
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/foods9020113
https://uvadoc.uva.es/handle/10324/52628
url https://doi.org/10.3390/foods9020113
https://uvadoc.uva.es/handle/10324/52628
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2304-8158/9/2/113
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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