Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129299554762752 |