Olive Ripening Phase Estimation based on Neural Networks

Autores
Mora, Marco; Aliaga, Jorge; Fredes, Claudio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Color of fruits is a relevant parameter to determine ripeness and optimal harvest time. For olives 6 ripening phases based on skin color distribution have been defined. A widely used method by the olive oil and table olives producers is to inspect the olive surface, and estimate the color and ripening phase visually. This method is simple but it is highly subjective and imprecise. This paper proposes a computational method to estimate the color and ripeness of an olive using digital images. A color scale for olives by means of samples of all ripening phases was developed. To represent the olive color, the histogram of the skin color was proposed as a descriptor. To decide the ripening phase, a classifier based on Neural Networks was implemented. The method allows estimating simply and accurately the olive ripening state, which enables to implement it in real production systems.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
olive ripening phases
color histogram
neural networks
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:10915/62909
Acceder
id SEDICI_41020c1a75064fcd1849d5cd0808589d
oai_identifier_str oai:sedici.unlp.edu.ar:10915/62909
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Olive Ripening Phase Estimation based on Neural NetworksMora, MarcoAliaga, JorgeFredes, ClaudioCiencias Informáticasolive ripening phasescolor histogramneural networksColor of fruits is a relevant parameter to determine ripeness and optimal harvest time. For olives 6 ripening phases based on skin color distribution have been defined. A widely used method by the olive oil and table olives producers is to inspect the olive surface, and estimate the color and ripening phase visually. This method is simple but it is highly subjective and imprecise. This paper proposes a computational method to estimate the color and ripeness of an olive using digital images. A color scale for olives by means of samples of all ripening phases was developed. To represent the olive color, the histogram of the skin color was proposed as a descriptor. To decide the ripening phase, a classifier based on Neural Networks was implemented. The method allows estimating simply and accurately the olive ripening state, which enables to implement it in real production systems.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/pdf127-140http://sedici.unlp.edu.ar/handle/10915/62909enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/CAI/CAI-12.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:UNLP2025-10-22T16:49:39Zoai:sedici.unlp.edu.ar:10915/62909Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:49:39.272SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Olive Ripening Phase Estimation based on Neural Networks
title Olive Ripening Phase Estimation based on Neural Networks
spellingShingle Olive Ripening Phase Estimation based on Neural Networks
Mora, Marco
Ciencias Informáticas
olive ripening phases
color histogram
neural networks
title_short Olive Ripening Phase Estimation based on Neural Networks
title_full Olive Ripening Phase Estimation based on Neural Networks
title_fullStr Olive Ripening Phase Estimation based on Neural Networks
title_full_unstemmed Olive Ripening Phase Estimation based on Neural Networks
title_sort Olive Ripening Phase Estimation based on Neural Networks
dc.creator.none.fl_str_mv Mora, Marco
Aliaga, Jorge
Fredes, Claudio
author Mora, Marco
author_facet Mora, Marco
Aliaga, Jorge
Fredes, Claudio
author_role author
author2 Aliaga, Jorge
Fredes, Claudio
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
olive ripening phases
color histogram
neural networks
topic Ciencias Informáticas
olive ripening phases
color histogram
neural networks
dc.description.none.fl_txt_mv Color of fruits is a relevant parameter to determine ripeness and optimal harvest time. For olives 6 ripening phases based on skin color distribution have been defined. A widely used method by the olive oil and table olives producers is to inspect the olive surface, and estimate the color and ripening phase visually. This method is simple but it is highly subjective and imprecise. This paper proposes a computational method to estimate the color and ripeness of an olive using digital images. A color scale for olives by means of samples of all ripening phases was developed. To represent the olive color, the histogram of the skin color was proposed as a descriptor. To decide the ripening phase, a classifier based on Neural Networks was implemented. The method allows estimating simply and accurately the olive ripening state, which enables to implement it in real production systems.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Color of fruits is a relevant parameter to determine ripeness and optimal harvest time. For olives 6 ripening phases based on skin color distribution have been defined. A widely used method by the olive oil and table olives producers is to inspect the olive surface, and estimate the color and ripening phase visually. This method is simple but it is highly subjective and imprecise. This paper proposes a computational method to estimate the color and ripeness of an olive using digital images. A color scale for olives by means of samples of all ripening phases was developed. To represent the olive color, the histogram of the skin color was proposed as a descriptor. To decide the ripening phase, a classifier based on Neural Networks was implemented. The method allows estimating simply and accurately the olive ripening state, which enables to implement it in real production systems.
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/10915/62909
url http://sedici.unlp.edu.ar/handle/10915/62909
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-12.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
127-140
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
_version_ 1846783043116728320
score 12.982451

Publicaciones similares