Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Wisconsin-
Autor(es): dc.contributorUS Forest Serv-
Autor(es): dc.contributorMississippi State Univ-
Autor(es): dc.contributorPurdue Univ-
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.creatorShmulsky, Rubin-
Autor(es): dc.creatorWiedenhoeft, Alex C.-
Data de aceite: dc.date.accessioned2022-08-04T21:58:25Z-
Data de disponibilização: dc.date.available2022-08-04T21:58:25Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-01-20-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fpls.2021.758455-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/218529-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/218529-
Descrição: dc.descriptionAvailability of and access to wood identification expertise or technology is a critical component for the design and implementation of practical, enforceable strategies for effective promotion, monitoring and incentivisation of sustainable practices and conservation efforts in the forest products value chain. To address this need in the context of the multi-billion-dollar North American wood products industry 22-class, image-based, deep learning models for the macroscopic identification of North American diffuse porous hardwoods were trained for deployment on the open-source, field-deployable XyloTron platform using transverse surface images of specimens from three different xylaria and evaluated on specimens from a fourth xylarium that did not contribute training data. Analysis of the model performance, in the context of the anatomy of the woods considered, demonstrates immediate readiness of the technology developed herein for field testing in a human-in-the-loop monitoring scenario. Also proposed are strategies for training, evaluating, and advancing the state-of-the-art for developing an expansive, continental scale model for all the North American hardwoods.-
Descrição: dc.descriptionUniv Wisconsin, Dept Bot, Madison, WI 53706 USA-
Descrição: dc.descriptionUS Forest Serv, Forest Prod Lab, Ctr Wood Anat Res, USDA, 1 Gifford Pinchot Dr, Madison, WI 53705 USA-
Descrição: dc.descriptionMississippi State Univ, Dept Sustainable Bioprod, Starkville, MS USA-
Descrição: dc.descriptionPurdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA-
Descrição: dc.descriptionUniv Estadual Paulista, Dept Ciencias Biol Bot, Botucatu, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Dept Ciencias Biol Bot, Botucatu, SP, Brazil-
Formato: dc.format13-
Idioma: dc.languageen-
Publicador: dc.publisherFrontiers Media Sa-
Relação: dc.relationFrontiers In Plant Science-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectwood identification-
Palavras-chave: dc.subjectillegal logging and timber trade-
Palavras-chave: dc.subjectXyloTron-
Palavras-chave: dc.subjectcomputer vision-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectdiffuse porous hardwoods-
Palavras-chave: dc.subjectsustainable wood products-
Título: dc.titleTowards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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