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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/62913
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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:UNLP2025-09-29T11:08:14Zoai: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:13292025-09-29 11:08:15.107SEDICI (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 |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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