Towards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoods

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Wisconsin-Madison-
Autor(es): dc.contributorUSDA Forest Service Products Laboratory-
Autor(es): dc.contributorMississippi State University-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRavindran, Prabu-
Autor(es): dc.creatorWade, Adam C.-
Autor(es): dc.creatorOwens, Frank C.-
Autor(es): dc.creatorShmulsky, Rubin-
Autor(es): dc.creatorWiedenhoeft, Alex C.-
Data de aceite: dc.date.accessioned2025-08-21T22:31:12Z-
Data de disponibilização: dc.date.available2025-08-21T22:31:12Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1139/cjfr-2022-0077-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241515-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241515-
Descrição: dc.descriptionWood identification is vitally important for ensuring the legality of North American hardwood value chains. Computer vision wood identification (CVWID) systems can identify wood without necessitating costly and time-consuming off-site visual inspections by highly trained wood anatomists. Previous work by Ravindran and colleagues presented macroscopic CVWID models for identification of North American diffuse porous hardwoods from 22 wood anatomically informed classes using the open-source XyloTron platform. This manuscript expands on that work by training and evaluating complementary 17-class XyloTron CVWID models for the identification of North American ring porous hardwoods ——woods that display spatial heterogeneity in earlywood and latewood pore size and distribution and other radial growth-rate-related features. Deep-learning models trained using 4045 images from 452 ring-porous wood specimens from four xylaria demonstrated 98% five-fold cross-validation accuracy. A field model trained on all the training data and subsequently tested on 198 specimens drawn from two additional xylaria achieved top-1 and top-2 predictions of 91.4% and 100%, respectively, and images devoid of earlywood, latewood, or broad rays did not greatly reduce the prediction accuracy. This study advocates for continued cooperation between wood anatomy and machine-learning experts for implementing and evaluating field-operational CVWID systems.-
Descrição: dc.descriptionDepartment of Botany University of Wisconsin-Madison, 430 Lincoln Drive-
Descrição: dc.descriptionCenter for Wood Anatomy Research USDA Forest Service Products Laboratory, 1 Gifford Pinchot Drive-
Descrição: dc.descriptionDepartment of Sustainable Bioproducts Mississippi State University, 201 Locksley Way-
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-
Formato: dc.format1014-1027-
Idioma: dc.languageen-
Relação: dc.relationCanadian Journal of Forest Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcomputer vision-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectdiffuse porous hardwoods-
Palavras-chave: dc.subjectillegal logging and timber trade-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectring-porous hardwoods-
Palavras-chave: dc.subjectsustainable wood products-
Palavras-chave: dc.subjectwood identification-
Palavras-chave: dc.subjectXyloTron-
Título: dc.titleTowards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoods-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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