Quantization of moisture content in yerba mate leaves through image processing

Autores
Leiva, Lucas; Acosta, Nelson
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Yerba Mate quality is defined by estimating the product moisture content. This value allows adjusting the production system, by controlling the stake of the dryer to ensure the product quality. Currently this process is done manually. However, this paper presents a first approach method to estimate the moisture contents of Yerba Mate leaves through image processing techniques. The output of the proposed system is established by a neural network MLPBP, which quantifies the level of moisture for a given sample. Also present the results of applying the proposed method to a set of 55 samples collected in a Yerba Mate production establishment.
Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
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:10915/23812
Acceder
id SEDICI_bff831ba4cee891fb733cd0c51f446b4
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23812
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Quantization of moisture content in yerba mate leaves through image processingLeiva, LucasAcosta, NelsonCiencias InformáticasNeural netsSignal processingReal timeYerba Mate moisture quantizationimage processingartificial neural networksThe Yerba Mate quality is defined by estimating the product moisture content. This value allows adjusting the production system, by controlling the stake of the dryer to ensure the product quality. Currently this process is done manually. However, this paper presents a first approach method to estimate the moisture contents of Yerba Mate leaves through image processing techniques. The output of the proposed system is established by a neural network MLPBP, which quantifies the level of moisture for a given sample. Also present the results of applying the proposed method to a set of 55 samples collected in a Yerba Mate production establishment.Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI)2012-10info: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/10915/23812enginfo: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:UNLP2025-09-03T10:28:25Zoai:sedici.unlp.edu.ar:10915/23812Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:25.222SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Quantization of moisture content in yerba mate leaves through image processing
title Quantization of moisture content in yerba mate leaves through image processing
spellingShingle Quantization of moisture content in yerba mate leaves through image processing
Leiva, Lucas
Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
title_short Quantization of moisture content in yerba mate leaves through image processing
title_full Quantization of moisture content in yerba mate leaves through image processing
title_fullStr Quantization of moisture content in yerba mate leaves through image processing
title_full_unstemmed Quantization of moisture content in yerba mate leaves through image processing
title_sort Quantization of moisture content in yerba mate leaves through image processing
dc.creator.none.fl_str_mv Leiva, Lucas
Acosta, Nelson
author Leiva, Lucas
author_facet Leiva, Lucas
Acosta, Nelson
author_role author
author2 Acosta, Nelson
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
topic Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
dc.description.none.fl_txt_mv The Yerba Mate quality is defined by estimating the product moisture content. This value allows adjusting the production system, by controlling the stake of the dryer to ensure the product quality. Currently this process is done manually. However, this paper presents a first approach method to estimate the moisture contents of Yerba Mate leaves through image processing techniques. The output of the proposed system is established by a neural network MLPBP, which quantifies the level of moisture for a given sample. Also present the results of applying the proposed method to a set of 55 samples collected in a Yerba Mate production establishment.
Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
description The Yerba Mate quality is defined by estimating the product moisture content. This value allows adjusting the production system, by controlling the stake of the dryer to ensure the product quality. Currently this process is done manually. However, this paper presents a first approach method to estimate the moisture contents of Yerba Mate leaves through image processing techniques. The output of the proposed system is established by a neural network MLPBP, which quantifies the level of moisture for a given sample. Also present the results of applying the proposed method to a set of 55 samples collected in a Yerba Mate production establishment.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
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/10915/23812
url http://sedici.unlp.edu.ar/handle/10915/23812
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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score 13.13397

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