Field-Deployable Computer Vision Wood Identification of Peruvian Timbers

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorUnited States Department of Agriculture Forest Service-
Autor(es): dc.contributorMississippi State University-
Autor(es): dc.contributorOregon State University-
Autor(es): dc.contributorUniversidad Nacional Agraria La Molina-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRavindran, Prabu-
Autor(es): dc.creatorOwens, Frank C.-
Autor(es): dc.creatorWade, Adam C.-
Autor(es): dc.creatorVega, Patricia-
Autor(es): dc.creatorMontenegro, Rolando-
Autor(es): dc.creatorShmulsky, Rubin-
Autor(es): dc.creatorWiedenhoeft, Alex C.-
Data de aceite: dc.date.accessioned2025-08-21T21:06:14Z-
Data de disponibilização: dc.date.available2025-08-21T21:06:14Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-06-02-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fpls.2021.647515-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/228983-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/228983-
Descrição: dc.descriptionIllegal logging is a major threat to forests in Peru, in the Amazon more broadly, and in the tropics globally. In Peru alone, more than two thirds of logging concessions showed unauthorized tree harvesting in natural protected areas and indigenous territories, and in 2016 more than half of exported lumber was of illegal origin. To help combat illegal logging and support legal timber trade in Peru we trained a convolutional neural network using transfer learning on images obtained from specimens in six xylaria using the open source, field-deployable XyloTron platform, for the classification of 228 Peruvian species into 24 anatomically informed and contextually relevant classes. The trained models achieved accuracies of 97% for five-fold cross validation, and 86.5 and 92.4% for top-1 and top-2 classification, respectively, on unique independent specimens from a xylarium that did not contribute training data. These results are the first multi-site, multi-user, multi-system-instantiation study for a national scale, computer vision wood identification system evaluated on independent scientific wood specimens. We demonstrate system readiness for evaluation in real-world field screening scenarios using this accurate, affordable, and scalable technology for monitoring, incentivizing, and monetizing legal and sustainable wood value chains.-
Descrição: dc.descriptionDepartment of Botany University of Wisconsin-
Descrição: dc.descriptionForest Products Laboratory Center for Wood Anatomy Research United States Department of Agriculture Forest Service-
Descrição: dc.descriptionDepartment of Sustainable Bioproducts Mississippi State University-
Descrição: dc.descriptionDepartment of Wood Science and Engineering Oregon State University-
Descrição: dc.descriptionDepartment of Wood Industry Universidad Nacional Agraria La Molina-
Descrição: dc.descriptionDepartment of Forestry and Natural Resources Purdue University-
Descrição: dc.descriptionDepartamento de Ciências Biolôgicas (Botânica) Universidade Estadual Paulista—Botucatu-
Descrição: dc.descriptionDepartamento de Ciências Biolôgicas (Botânica) Universidade Estadual Paulista—Botucatu-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Plant Science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcomputer vision-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectillegal logging and timber trade-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectwood identification-
Palavras-chave: dc.subjectXyloTron-
Título: dc.titleField-Deployable Computer Vision Wood Identification of Peruvian Timbers-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.