Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system

Detalhes bibliográficos
Autor(a) principal: Guedes, Wesley Nascimento [UNESP]
Data de Publicação: 2019
Outros Autores: Pereira, Fabíola Manhas Verbi [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.compag.2018.11.039
http://hdl.handle.net/11449/187119
Resumo: Specific amounts of solid impurities in raw sugarcane need to be detected before raw materials are carried into mills. Solid impurities come from the plant, e.g., green and dry leaves and soil. This study proposed to classify sugarcane via a new strategy using a well-established method that combines digital images converted into ten color-scale color histograms of red (R), green (G) and blue (B), RGB; hue (H), saturation (S) and value (v), HSV; relative colors of RGB, rgb; and luminosity (L) with multivariate classification methods. Sampling was performed using a mixture design that comprised 122 different combinations of sugarcane stalks, vegetal plant parts and soil to achieve 100 wt% for evaluating the desirable and undesirable situations for the solid impurity amounts. Classical algorithms, such as soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and k nearest neighbors (kNN), were used to perform the calculations. Receive operating characteristic (ROC) revealed the high sensitivity and specificity of the three algorithms using the color histogram data. The outstanding result was the ability to classify sugarcane content higher than 85 wt%, which is considered high-quality raw material by cane mills.
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spelling Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging systemBioenergyChemometricsDigital imagesSolid impuritiesSugarcaneSpecific amounts of solid impurities in raw sugarcane need to be detected before raw materials are carried into mills. Solid impurities come from the plant, e.g., green and dry leaves and soil. This study proposed to classify sugarcane via a new strategy using a well-established method that combines digital images converted into ten color-scale color histograms of red (R), green (G) and blue (B), RGB; hue (H), saturation (S) and value (v), HSV; relative colors of RGB, rgb; and luminosity (L) with multivariate classification methods. Sampling was performed using a mixture design that comprised 122 different combinations of sugarcane stalks, vegetal plant parts and soil to achieve 100 wt% for evaluating the desirable and undesirable situations for the solid impurity amounts. Classical algorithms, such as soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and k nearest neighbors (kNN), were used to perform the calculations. Receive operating characteristic (ROC) revealed the high sensitivity and specificity of the three algorithms using the color histogram data. The outstanding result was the ability to classify sugarcane content higher than 85 wt%, which is considered high-quality raw material by cane mills.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Bioenergy Research Institute (IPBEN) Institute of Chemistry São Paulo State University (UNESP)Department of Chemistry Idaho State UniversityBioenergy Research Institute (IPBEN) Institute of Chemistry São Paulo State University (UNESP)FAPESP: 2016/00779-6FAPESP: 2017/05550-0Universidade Estadual Paulista (Unesp)Idaho State UniversityGuedes, Wesley Nascimento [UNESP]Pereira, Fabíola Manhas Verbi [UNESP]2019-10-06T15:26:06Z2019-10-06T15:26:06Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article307-311http://dx.doi.org/10.1016/j.compag.2018.11.039Computers and Electronics in Agriculture, v. 156, p. 307-311.0168-1699http://hdl.handle.net/11449/18711910.1016/j.compag.2018.11.0392-s2.0-8505743966057044454736540240000-0002-8117-2108Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers and Electronics in Agricultureinfo:eu-repo/semantics/openAccess2021-10-23T05:43:34Zoai:repositorio.unesp.br:11449/187119Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:14:09.915297Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
title Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
spellingShingle Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
Guedes, Wesley Nascimento [UNESP]
Bioenergy
Chemometrics
Digital images
Solid impurities
Sugarcane
title_short Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
title_full Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
title_fullStr Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
title_full_unstemmed Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
title_sort Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
author Guedes, Wesley Nascimento [UNESP]
author_facet Guedes, Wesley Nascimento [UNESP]
Pereira, Fabíola Manhas Verbi [UNESP]
author_role author
author2 Pereira, Fabíola Manhas Verbi [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Idaho State University
dc.contributor.author.fl_str_mv Guedes, Wesley Nascimento [UNESP]
Pereira, Fabíola Manhas Verbi [UNESP]
dc.subject.por.fl_str_mv Bioenergy
Chemometrics
Digital images
Solid impurities
Sugarcane
topic Bioenergy
Chemometrics
Digital images
Solid impurities
Sugarcane
description Specific amounts of solid impurities in raw sugarcane need to be detected before raw materials are carried into mills. Solid impurities come from the plant, e.g., green and dry leaves and soil. This study proposed to classify sugarcane via a new strategy using a well-established method that combines digital images converted into ten color-scale color histograms of red (R), green (G) and blue (B), RGB; hue (H), saturation (S) and value (v), HSV; relative colors of RGB, rgb; and luminosity (L) with multivariate classification methods. Sampling was performed using a mixture design that comprised 122 different combinations of sugarcane stalks, vegetal plant parts and soil to achieve 100 wt% for evaluating the desirable and undesirable situations for the solid impurity amounts. Classical algorithms, such as soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and k nearest neighbors (kNN), were used to perform the calculations. Receive operating characteristic (ROC) revealed the high sensitivity and specificity of the three algorithms using the color histogram data. The outstanding result was the ability to classify sugarcane content higher than 85 wt%, which is considered high-quality raw material by cane mills.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-06T15:26:06Z
2019-10-06T15:26:06Z
2019-01-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.compag.2018.11.039
Computers and Electronics in Agriculture, v. 156, p. 307-311.
0168-1699
http://hdl.handle.net/11449/187119
10.1016/j.compag.2018.11.039
2-s2.0-85057439660
5704445473654024
0000-0002-8117-2108
url http://dx.doi.org/10.1016/j.compag.2018.11.039
http://hdl.handle.net/11449/187119
identifier_str_mv Computers and Electronics in Agriculture, v. 156, p. 307-311.
0168-1699
10.1016/j.compag.2018.11.039
2-s2.0-85057439660
5704445473654024
0000-0002-8117-2108
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Computers and Electronics in Agriculture
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 307-311
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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