Quantization of moisture content in yerba mate leaves through image processing

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 estim...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Leiva, Lucas, Acosta, Nelson
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23812
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23812
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
spellingShingle Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
Leiva, Lucas
Acosta, Nelson
Quantization of moisture content in yerba mate leaves through image processing
topic_facet Ciencias Informáticas
Neural nets
Signal processing
Real time
Yerba Mate moisture quantization
image processing
artificial neural networks
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.
format Objeto de conferencia
Objeto de conferencia
author Leiva, Lucas
Acosta, Nelson
author_facet Leiva, Lucas
Acosta, Nelson
author_sort Leiva, Lucas
title Quantization of moisture content in yerba mate leaves through image processing
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
publishDate 2012
url http://sedici.unlp.edu.ar/handle/10915/23812
work_keys_str_mv AT leivalucas quantizationofmoisturecontentinyerbamateleavesthroughimageprocessing
AT acostanelson quantizationofmoisturecontentinyerbamateleavesthroughimageprocessing
bdutipo_str Repositorios
_version_ 1764820466257625088

Ejemplares similares