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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/42325
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:01:23Zoai: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:13292025-09-29 11:01:23.306SEDICI (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 |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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application/pdf |
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