Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861 |
Resumo: | A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels. |
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Acta Scientiarum. Agronomy (Online) |
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Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networksWhite Wholes (WW) grade cashew kernel imagesfeature extractionartificial neural networksclassification A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels. Universidade Estadual de Maringá2016-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionImage Analysisapplication/pdfapplication/mswordapplication/mswordapplication/mswordhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/2786110.4025/actasciagron.v38i2.27861Acta Scientiarum. Agronomy; Vol 38 No 2 (2016); 145-155Acta Scientiarum. Agronomy; v. 38 n. 2 (2016); 145-1551807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/pdf_109http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/751375143827http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/751375143829http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/751375143830Ganganagowdar, Narendra VeranagoudaSiddaramappa, Hareesha Katiganereinfo:eu-repo/semantics/openAccess2022-02-16T21:48:08Zoai:periodicos.uem.br/ojs:article/27861Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2022-02-16T21:48:08Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
title |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
spellingShingle |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks Ganganagowdar, Narendra Veranagouda White Wholes (WW) grade cashew kernel images feature extraction artificial neural networks classification |
title_short |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
title_full |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
title_fullStr |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
title_full_unstemmed |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
title_sort |
Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks |
author |
Ganganagowdar, Narendra Veranagouda |
author_facet |
Ganganagowdar, Narendra Veranagouda Siddaramappa, Hareesha Katiganere |
author_role |
author |
author2 |
Siddaramappa, Hareesha Katiganere |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Ganganagowdar, Narendra Veranagouda Siddaramappa, Hareesha Katiganere |
dc.subject.por.fl_str_mv |
White Wholes (WW) grade cashew kernel images feature extraction artificial neural networks classification |
topic |
White Wholes (WW) grade cashew kernel images feature extraction artificial neural networks classification |
description |
A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Image Analysis |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861 10.4025/actasciagron.v38i2.27861 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861 |
identifier_str_mv |
10.4025/actasciagron.v38i2.27861 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/pdf_109 http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/751375143827 http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/751375143829 http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/751375143830 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/msword application/msword application/msword |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 38 No 2 (2016); 145-155 Acta Scientiarum. Agronomy; v. 38 n. 2 (2016); 145-155 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
_version_ |
1799305909412823040 |