A Method to estimate Grape Phenolic Maturity based on Color Features

Autores
Avila, Felipe; Mora, Marco; Zuñiga, Alex; Oyarce, Miguel; Fredes, Claudio
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.
Eje: XII Workshop de Computación gráfica, Imágenes y Visualización
Red de Universidades con Carreras de Informática (RedUNCI)
Materia
Ciencias Informáticas
seed image
phenolic maturity
Graphical environments
neural network
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/42325
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A Method to estimate Grape Phenolic Maturity based on Color FeaturesAvila, FelipeMora, MarcoZuñiga, AlexOyarce, MiguelFredes, ClaudioCiencias Informáticasseed imagephenolic maturityGraphical environmentsneural networkThe phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.Eje: XII Workshop de Computación gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras de Informática (RedUNCI)2014-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/42325enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:53:54Zoai:sedici.unlp.edu.ar:10915/42325Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:53:54.277SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A Method to estimate Grape Phenolic Maturity based on Color Features
title A Method to estimate Grape Phenolic Maturity based on Color Features
spellingShingle A Method to estimate Grape Phenolic Maturity based on Color Features
Avila, Felipe
Ciencias Informáticas
seed image
phenolic maturity
Graphical environments
neural network
title_short A Method to estimate Grape Phenolic Maturity based on Color Features
title_full A Method to estimate Grape Phenolic Maturity based on Color Features
title_fullStr A Method to estimate Grape Phenolic Maturity based on Color Features
title_full_unstemmed A Method to estimate Grape Phenolic Maturity based on Color Features
title_sort A Method to estimate Grape Phenolic Maturity based on Color Features
dc.creator.none.fl_str_mv Avila, Felipe
Mora, Marco
Zuñiga, Alex
Oyarce, Miguel
Fredes, Claudio
author Avila, Felipe
author_facet Avila, Felipe
Mora, Marco
Zuñiga, Alex
Oyarce, Miguel
Fredes, Claudio
author_role author
author2 Mora, Marco
Zuñiga, Alex
Oyarce, Miguel
Fredes, Claudio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
seed image
phenolic maturity
Graphical environments
neural network
topic Ciencias Informáticas
seed image
phenolic maturity
Graphical environments
neural network
dc.description.none.fl_txt_mv The phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.
Eje: XII Workshop de Computación gráfica, Imágenes y Visualización
Red de Universidades con Carreras de Informática (RedUNCI)
description The phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
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/10915/42325
url http://sedici.unlp.edu.ar/handle/10915/42325
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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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