Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorGroup of Alternative Analytical Approaches-
Autor(es): dc.contributorNatl. Inst. of Alternative Technol. for Detection Toxicological Assess. and Removal of Micropollutants and Radioactive Substances-
Autor(es): dc.creatorDos Santos, Lucas Janoni [UNESP]-
Autor(es): dc.creatorFilletti, Erica Regina [UNESP]-
Autor(es): dc.creatorPereira, Fabiola Manhas Verbi [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:10:19Z-
Data de disponibilização: dc.date.available2022-08-04T22:10:19Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.26850/1678-4618eqj.v46.3.2021.p49-54-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221940-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221940-
Descrição: dc.descriptionAn investigation dedicated to evaluating a big issue in biorefineries, solid impurity in raw sugarcane, is presented. This relevant industrial sector requests a high-frequency, low-cost, and noninvasive method. Then, the developed method uses the averaged color values of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity) from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity-vegetal parts (green and dry leaves) and soil. The solid mixture of samples was prepared considering desirable and undesirable scenarios for the solid impurity amounts. The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: From 90 to 100 wt% and from 41 to 87 wt%. Low-computational cost and a simple setup for image acquisition method could screen solid impurity in sugarcane shipments as a promising application.-
Descrição: dc.descriptionSao Paulo State University Institute of Chemistry-
Descrição: dc.descriptionBioenergy Research Institute Group of Alternative Analytical Approaches-
Descrição: dc.descriptionNatl. Inst. of Alternative Technol. for Detection Toxicological Assess. and Removal of Micropollutants and Radioactive Substances-
Descrição: dc.descriptionSao Paulo State University Institute of Chemistry-
Formato: dc.format49-54-
Idioma: dc.languageen-
Relação: dc.relationEcletica Quimica-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectANN-
Palavras-chave: dc.subjectBioenergy-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectImage-
Palavras-chave: dc.subjectSugarcane-
Título: dc.titleArtificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.