Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/10608 |
Resumo: | One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry. |
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Development and evaluation of a small-scale apple sorting machine equipped with a smart vision systemApple sortingBruiseClassificationComputer visionk-NN classifierOne of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry.MDPIBiblioteca Digital do IPBBaneh, Nesar MohammadiNavid, HosseinKafashan, JalalFouladi, HatefGonzales-Barron, Ursula2014-09-25T16:25:38Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/10608engBaneh, Nesar Mohammadi; Navid, Hossein; Kafashan, Jalal; Fouladi, Hatef; Gonzales-Barron, Ursula (2023). Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system. AgriEngineering. eISSN 2624-7402. 5:1, p. 1-1510.3390/agriengineering50100312624-7402info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-07T01:18:01Zoai:bibliotecadigital.ipb.pt:10198/10608Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:01:27.461339Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
title |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
spellingShingle |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system Baneh, Nesar Mohammadi Apple sorting Bruise Classification Computer vision k-NN classifier |
title_short |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
title_full |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
title_fullStr |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
title_full_unstemmed |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
title_sort |
Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system |
author |
Baneh, Nesar Mohammadi |
author_facet |
Baneh, Nesar Mohammadi Navid, Hossein Kafashan, Jalal Fouladi, Hatef Gonzales-Barron, Ursula |
author_role |
author |
author2 |
Navid, Hossein Kafashan, Jalal Fouladi, Hatef Gonzales-Barron, Ursula |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Baneh, Nesar Mohammadi Navid, Hossein Kafashan, Jalal Fouladi, Hatef Gonzales-Barron, Ursula |
dc.subject.por.fl_str_mv |
Apple sorting Bruise Classification Computer vision k-NN classifier |
topic |
Apple sorting Bruise Classification Computer vision k-NN classifier |
description |
One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09-25T16:25:38Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/10608 |
url |
http://hdl.handle.net/10198/10608 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Baneh, Nesar Mohammadi; Navid, Hossein; Kafashan, Jalal; Fouladi, Hatef; Gonzales-Barron, Ursula (2023). Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system. AgriEngineering. eISSN 2624-7402. 5:1, p. 1-15 10.3390/agriengineering5010031 2624-7402 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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