Implementation of Apple’s automatic sorting system based on machine learning

Detalhes bibliográficos
Autor(a) principal: ZOU,ZhiYong
Data de Publicação: 2022
Outros Autores: LONG,Tao, WANG,Qi, WANG,Li, CHEN,Jie, ZOU,Bing, XU,Lijia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Food Science and Technology (Campinas)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101164
Resumo: Abstract In order to reduce post-harvest losses, the classification of fresh apples is crucial. Taking the hierarchical transmission control system as the object, the research was carried out on the verification of the bus network can flexibly expand the motor equipment, the stable and reliable operation of the motor, and the accuracy of Apple's classification. Combine Labview virtual instrument technology to realize the design of Apple's hierarchical transmission control system based on Controller Area Network technology. Fuzzy PID and traditional PID algorithms are used to simulate and realize the operation of brushless DC motor, and compare the advantages of brushless DC motor control based on fuzzy PID to ensure the safe and stable operation of the system. Using the machine learning algorithms model for color detection, the Support Vector Machine algorithm model finally achieved the classification of the three types of apple samples with a recognition rate of 96.7%.
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spelling Implementation of Apple’s automatic sorting system based on machine learningbrushless DC motorfuzzy controlmachine learninggradingAbstract In order to reduce post-harvest losses, the classification of fresh apples is crucial. Taking the hierarchical transmission control system as the object, the research was carried out on the verification of the bus network can flexibly expand the motor equipment, the stable and reliable operation of the motor, and the accuracy of Apple's classification. Combine Labview virtual instrument technology to realize the design of Apple's hierarchical transmission control system based on Controller Area Network technology. Fuzzy PID and traditional PID algorithms are used to simulate and realize the operation of brushless DC motor, and compare the advantages of brushless DC motor control based on fuzzy PID to ensure the safe and stable operation of the system. Using the machine learning algorithms model for color detection, the Support Vector Machine algorithm model finally achieved the classification of the three types of apple samples with a recognition rate of 96.7%.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101164Food Science and Technology v.42 2022reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.24922info:eu-repo/semantics/openAccessZOU,ZhiYongLONG,TaoWANG,QiWANG,LiCHEN,JieZOU,BingXU,Lijiaeng2022-05-30T00:00:00Zoai:scielo:S0101-20612022000101164Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-05-30T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false
dc.title.none.fl_str_mv Implementation of Apple’s automatic sorting system based on machine learning
title Implementation of Apple’s automatic sorting system based on machine learning
spellingShingle Implementation of Apple’s automatic sorting system based on machine learning
ZOU,ZhiYong
brushless DC motor
fuzzy control
machine learning
grading
title_short Implementation of Apple’s automatic sorting system based on machine learning
title_full Implementation of Apple’s automatic sorting system based on machine learning
title_fullStr Implementation of Apple’s automatic sorting system based on machine learning
title_full_unstemmed Implementation of Apple’s automatic sorting system based on machine learning
title_sort Implementation of Apple’s automatic sorting system based on machine learning
author ZOU,ZhiYong
author_facet ZOU,ZhiYong
LONG,Tao
WANG,Qi
WANG,Li
CHEN,Jie
ZOU,Bing
XU,Lijia
author_role author
author2 LONG,Tao
WANG,Qi
WANG,Li
CHEN,Jie
ZOU,Bing
XU,Lijia
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv ZOU,ZhiYong
LONG,Tao
WANG,Qi
WANG,Li
CHEN,Jie
ZOU,Bing
XU,Lijia
dc.subject.por.fl_str_mv brushless DC motor
fuzzy control
machine learning
grading
topic brushless DC motor
fuzzy control
machine learning
grading
description Abstract In order to reduce post-harvest losses, the classification of fresh apples is crucial. Taking the hierarchical transmission control system as the object, the research was carried out on the verification of the bus network can flexibly expand the motor equipment, the stable and reliable operation of the motor, and the accuracy of Apple's classification. Combine Labview virtual instrument technology to realize the design of Apple's hierarchical transmission control system based on Controller Area Network technology. Fuzzy PID and traditional PID algorithms are used to simulate and realize the operation of brushless DC motor, and compare the advantages of brushless DC motor control based on fuzzy PID to ensure the safe and stable operation of the system. Using the machine learning algorithms model for color detection, the Support Vector Machine algorithm model finally achieved the classification of the three types of apple samples with a recognition rate of 96.7%.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101164
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101164
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/fst.24922
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
dc.source.none.fl_str_mv Food Science and Technology v.42 2022
reponame:Food Science and Technology (Campinas)
instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
instacron:SBCTA
instname_str Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
instacron_str SBCTA
institution SBCTA
reponame_str Food Science and Technology (Campinas)
collection Food Science and Technology (Campinas)
repository.name.fl_str_mv Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
repository.mail.fl_str_mv ||revista@sbcta.org.br
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