A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds

Autores
Avila, Felipe; Mora, Marco; Oyarce, Miguel; Zuñiga, Alex; Fredes, Claudio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Color scales are a powerful tool used in agriculture for estimate maturity of fruits. Fruit maturity is an important parameter to determine the harvest time. Typically, to obtain the maturity grade, a human expert visually associates the fruit color with a color present in the scale. In this paper, a computer-based method to create color scales is proposed. The proposed method performs a multidimensional regression based on Support Vector Regression (SVR) to generate color scales. The experimen-tation considers two color scales examples, the first one for grape seeds, the second one for olives. Grape seed data set contains 250 samples and olives data set has 200 samples. Color scales developed by SVR were validated through K-fold Cross Vali-dation method, using mean squared error as performance function. The proposed method generates scales that adequately follow the evolution of color in the fruit maturity process, provides a tool to define different phenolic pre-harvest stages, which may be of interest to the human expert.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
color scales
fruit maturity
support vector regression
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/62913
Acceder
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network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape SeedsAvila, FelipeMora, MarcoOyarce, MiguelZuñiga, AlexFredes, ClaudioCiencias Informáticascolor scalesfruit maturitysupport vector regressionColor scales are a powerful tool used in agriculture for estimate maturity of fruits. Fruit maturity is an important parameter to determine the harvest time. Typically, to obtain the maturity grade, a human expert visually associates the fruit color with a color present in the scale. In this paper, a computer-based method to create color scales is proposed. The proposed method performs a multidimensional regression based on Support Vector Regression (SVR) to generate color scales. The experimen-tation considers two color scales examples, the first one for grape seeds, the second one for olives. Grape seed data set contains 250 samples and olives data set has 200 samples. Color scales developed by SVR were validated through K-fold Cross Vali-dation method, using mean squared error as performance function. The proposed method generates scales that adequately follow the evolution of color in the fruit maturity process, provides a tool to define different phenolic pre-harvest stages, which may be of interest to the human expert.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf165-165http://sedici.unlp.edu.ar/handle/10915/62913enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/CAI/CAI-15.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525- 0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:00:43Zoai:sedici.unlp.edu.ar:10915/62913Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:00:43.286SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
title A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
spellingShingle A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
Avila, Felipe
Ciencias Informáticas
color scales
fruit maturity
support vector regression
title_short A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
title_full A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
title_fullStr A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
title_full_unstemmed A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
title_sort A Method to Construct Fruit Maturity Color Scales based on Support Vector Machines for Regression: Application to Olives and Grape Seeds
dc.creator.none.fl_str_mv Avila, Felipe
Mora, Marco
Oyarce, Miguel
Zuñiga, Alex
Fredes, Claudio
author Avila, Felipe
author_facet Avila, Felipe
Mora, Marco
Oyarce, Miguel
Zuñiga, Alex
Fredes, Claudio
author_role author
author2 Mora, Marco
Oyarce, Miguel
Zuñiga, Alex
Fredes, Claudio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
color scales
fruit maturity
support vector regression
topic Ciencias Informáticas
color scales
fruit maturity
support vector regression
dc.description.none.fl_txt_mv Color scales are a powerful tool used in agriculture for estimate maturity of fruits. Fruit maturity is an important parameter to determine the harvest time. Typically, to obtain the maturity grade, a human expert visually associates the fruit color with a color present in the scale. In this paper, a computer-based method to create color scales is proposed. The proposed method performs a multidimensional regression based on Support Vector Regression (SVR) to generate color scales. The experimen-tation considers two color scales examples, the first one for grape seeds, the second one for olives. Grape seed data set contains 250 samples and olives data set has 200 samples. Color scales developed by SVR were validated through K-fold Cross Vali-dation method, using mean squared error as performance function. The proposed method generates scales that adequately follow the evolution of color in the fruit maturity process, provides a tool to define different phenolic pre-harvest stages, which may be of interest to the human expert.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Color scales are a powerful tool used in agriculture for estimate maturity of fruits. Fruit maturity is an important parameter to determine the harvest time. Typically, to obtain the maturity grade, a human expert visually associates the fruit color with a color present in the scale. In this paper, a computer-based method to create color scales is proposed. The proposed method performs a multidimensional regression based on Support Vector Regression (SVR) to generate color scales. The experimen-tation considers two color scales examples, the first one for grape seeds, the second one for olives. Grape seed data set contains 250 samples and olives data set has 200 samples. Color scales developed by SVR were validated through K-fold Cross Vali-dation method, using mean squared error as performance function. The proposed method generates scales that adequately follow the evolution of color in the fruit maturity process, provides a tool to define different phenolic pre-harvest stages, which may be of interest to the human expert.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/62913
url http://sedici.unlp.edu.ar/handle/10915/62913
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/CAI/CAI-15.pdf
info:eu-repo/semantics/altIdentifier/issn/2525- 0949
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.format.none.fl_str_mv application/pdf
165-165
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.22299

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