Terahertz imaging and machine learning in the classification of coffee beans

Descripción del Articulo

El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.
Detalles Bibliográficos
Autores: Uceda, Patricia, Yoshida, Hideaki, Castillo, Pedro
Formato: objeto de conferencia
Fecha de Publicación:2021
Institución:Universidad Privada del Norte
Repositorio:UPN-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.upn.edu.pe:11537/27168
Enlace del recurso:https://hdl.handle.net/11537/27168
https://doi.org/10.1007/978-3-030-75680-2_94
Nivel de acceso:acceso abierto
Materia:Inteligencia artificial
Café
Clasificación
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dc.title.es_PE.fl_str_mv Terahertz imaging and machine learning in the classification of coffee beans
title Terahertz imaging and machine learning in the classification of coffee beans
spellingShingle Terahertz imaging and machine learning in the classification of coffee beans
Uceda, Patricia
Inteligencia artificial
Café
Clasificación
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Terahertz imaging and machine learning in the classification of coffee beans
title_full Terahertz imaging and machine learning in the classification of coffee beans
title_fullStr Terahertz imaging and machine learning in the classification of coffee beans
title_full_unstemmed Terahertz imaging and machine learning in the classification of coffee beans
title_sort Terahertz imaging and machine learning in the classification of coffee beans
author Uceda, Patricia
author_facet Uceda, Patricia
Yoshida, Hideaki
Castillo, Pedro
author_role author
author2 Yoshida, Hideaki
Castillo, Pedro
author2_role author
author
dc.contributor.author.fl_str_mv Uceda, Patricia
Yoshida, Hideaki
Castillo, Pedro
dc.subject.es_PE.fl_str_mv Inteligencia artificial
Café
Clasificación
topic Inteligencia artificial
Café
Clasificación
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-12T23:05:22Z
dc.date.available.none.fl_str_mv 2021-07-12T23:05:22Z
dc.date.issued.fl_str_mv 2021-06-15
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dc.identifier.citation.es_PE.fl_str_mv Uceda, P., Yoshida, H., & Castillo, P. (2021). Terahertz imaging and machine learning in the classification of coffee beans. Proceedings of the 6th Brazilian Technology Symposium. Smart Innovation, Systems and Technologies, 233, 854-861. https://doi.org/10.1007/978-3-030-75680-2_94
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11537/27168
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-030-75680-2_94
identifier_str_mv Uceda, P., Yoshida, H., & Castillo, P. (2021). Terahertz imaging and machine learning in the classification of coffee beans. Proceedings of the 6th Brazilian Technology Symposium. Smart Innovation, Systems and Technologies, 233, 854-861. https://doi.org/10.1007/978-3-030-75680-2_94
url https://hdl.handle.net/11537/27168
https://doi.org/10.1007/978-3-030-75680-2_94
dc.language.iso.es_PE.fl_str_mv eng
language eng
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dc.rights.uri.*.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/3.0/us/
eu_rights_str_mv openAccess
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América
https://creativecommons.org/licenses/by-nc-sa/3.0/us/
dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Springer
dc.publisher.country.es_PE.fl_str_mv CH
dc.source.es_PE.fl_str_mv Universidad Privada del Norte
Repositorio Institucional - UPN
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spelling Uceda, PatriciaYoshida, HideakiCastillo, Pedro2021-07-12T23:05:22Z2021-07-12T23:05:22Z2021-06-15Uceda, P., Yoshida, H., & Castillo, P. (2021). Terahertz imaging and machine learning in the classification of coffee beans. Proceedings of the 6th Brazilian Technology Symposium. Smart Innovation, Systems and Technologies, 233, 854-861. https://doi.org/10.1007/978-3-030-75680-2_94https://hdl.handle.net/11537/27168https://doi.org/10.1007/978-3-030-75680-2_94El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.ABSTRACT The geographical origin of coffee beans represents an effect on the attributes and quality of the product due to the different soil and weather conditions for a specific location. Therefore, the development of methods for rapid classification and authentication of coffee beans based on their geographical origin is essential. This research was done with the purpose of determining the capacity of coffee (Coffea arabica) varieties classification with the use of Terahertz (THz) imaging and machine learning. THz images of coffee beans samples from 3 different geographical origins were acquired with a time-domain spectrometer and then used to measure the classification performance of methods such as neural networks, random forests, and support vector machines. The results obtained reached an accuracy up to 91.2%, which showed that the use of THz imaging and machine learning is an effective method for the non-destructive analysis of coffee variables and classification based on geographical origin.Trujillo San Isidroapplication/pdfengSpringerCHinfo:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 3.0 Estados Unidos de Américahttps://creativecommons.org/licenses/by-nc-sa/3.0/us/Universidad Privada del NorteRepositorio Institucional - UPNreponame:UPN-Institucionalinstname:Universidad Privada del Norteinstacron:UPNInteligencia artificialCaféClasificaciónhttps://purl.org/pe-repo/ocde/ford#2.02.04Terahertz imaging and machine learning in the classification of coffee beansinfo:eu-repo/semantics/conferenceObjectCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81037https://repositorio.upn.edu.pe/bitstream/11537/27168/1/license_rdf80294ba9ff4c5b4f07812ee200fbc42fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.upn.edu.pe/bitstream/11537/27168/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5211537/27168oai:repositorio.upn.edu.pe:11537/271682021-07-12 18:05:27.511Repositorio Institucional UPNjordan.rivero@upn.edu.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Terahertz imaging and machine learning in the classification of coffee beans
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