Imaged based identification of colombian timbers using the xylotron: A proof of concept international partnership

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorForest Products Laboratory-
Autor(es): dc.contributorUniversidad Distrital Francisco Jose de Caldas-
Autor(es): dc.contributorUniversity of Torino-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorMississippi State University-
Autor(es): dc.creatorArévalo, Rafael-
Autor(es): dc.creatorPulido R., Esperanza N.-
Autor(es): dc.creatorSolórzano G., Juan F.-
Autor(es): dc.creatorSoares, Richard-
Autor(es): dc.creatorRuffinatto, Flavio-
Autor(es): dc.creatorRavindran, Prabu-
Autor(es): dc.creatorWiedenhoeft, Alex C. [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:09:10Z-
Data de disponibilização: dc.date.available2022-08-04T22:09:10Z-
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/10.14483/2256201X.16700-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221625-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221625-
Descrição: dc.descriptionField deployable computer vision wood identification systems can be relevant in combating illegal logging in the real world. This work used 764 xylarium specimens from 84 taxa to develop an image data set to train a classifier and identify 14 commercial Colombian timbers. We took images of specimens from various xylaria outside Colombia, trained and evaluated an initial identification model and then collected additional images from a Colombian xylarium (BOFw) and incorporated these images to refine and produce a final model. The specimen classification accuracy of this final model was ~ 97%, which demonstrates that including local specimens can augment the accuracy and reliability of the XyloTron system. Our study demonstrates the first deployable computer vision model for wood identification in Colombia, which is developed on a timescale of months rather than years by leveraging on international cooperation. We conclude that field testing and advanced forensic and machine learning training are the next logical steps.-
Descrição: dc.descriptionDepartment of Botany University of Wisconsin-
Descrição: dc.descriptionCenter for Wood Anatomy Research USDA Forest Service Forest Products Laboratory-
Descrição: dc.descriptionFacultad de Medio Ambiente y Recursos Naturales Universidad Distrital Francisco Jose de Caldas-
Descrição: dc.descriptionDISAFA University of Torino, Largo Paolo Braccini 2-
Descrição: dc.descriptionDepartment of Forestry and Natural Resources Purdue University-
Descrição: dc.descriptionDepartamento de Ciências Biolôgicas (Botânica) Universidade Estadual Paulista-
Descrição: dc.descriptionDepartment of Sustainable Bioproducts Mississippi State University-
Descrição: dc.descriptionDepartamento de Ciências Biolôgicas (Botânica) Universidade Estadual Paulista-
Formato: dc.format5-16-
Idioma: dc.languageen-
Relação: dc.relationColombia Forestal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectForensic wood anatomy-
Palavras-chave: dc.subjectMachine Learning-
Palavras-chave: dc.subjectTransfer learning-
Palavras-chave: dc.subjectWood identification-
Título: dc.titleImaged based identification of colombian timbers using the xylotron: A proof of concept international partnership-
Título: dc.titleIdentificación de maderas colombianas utilizando el Xylotron: Prueba de concepto de una colaboración internacional-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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