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

Página do item simplificado
dc.contributor.authorRavindran, Prabu
dc.contributor.authorWade, Adam C.
dc.contributor.authorOwens, Frank C.
dc.contributor.authorShmulsky, Rubin
dc.contributor.authorWiedenhoeft, Alex C. [UNESP]
dc.contributor.institutionUniversity of Wisconsin-Madison
dc.contributor.institutionUSDA Forest Service Products Laboratory
dc.contributor.institutionMississippi State University
dc.contributor.institutionPurdue University
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-03-01T21:07:32Z
dc.date.available2023-03-01T21:07:32Z
dc.date.issued2022-01-01
dc.description.abstractWood 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.en
dc.description.affiliationDepartment of Botany University of Wisconsin-Madison, 430 Lincoln Drive
dc.description.affiliationCenter for Wood Anatomy Research USDA Forest Service Products Laboratory, 1 Gifford Pinchot Drive
dc.description.affiliationDepartment of Sustainable Bioproducts Mississippi State University, 201 Locksley Way
dc.description.affiliationDepartment of Forestry and Natural Resources Purdue University
dc.description.affiliationDepartamento de Ciências Biolôgicas (Botânica) Universidade Estadual Paulista – Botucatu
dc.description.affiliationUnespDepartamento de Ciências Biolôgicas (Botânica) Universidade Estadual Paulista – Botucatu
dc.format.extent1014-1027
dc.identifierhttp://dx.doi.org/10.1139/cjfr-2022-0077
dc.identifier.citationCanadian Journal of Forest Research, v. 52, n. 7, p. 1014-1027, 2022.
dc.identifier.doi10.1139/cjfr-2022-0077
dc.identifier.issn1208-6037
dc.identifier.issn0045-5067
dc.identifier.scopus2-s2.0-85136118532
dc.identifier.urihttp://hdl.handle.net/11449/241515
dc.language.isoeng
dc.relation.ispartofCanadian Journal of Forest Research
dc.sourceScopus
dc.subjectcomputer vision
dc.subjectdeep learning
dc.subjectdiffuse porous hardwoods
dc.subjectillegal logging and timber trade
dc.subjectmachine learning
dc.subjectring-porous hardwoods
dc.subjectsustainable wood products
dc.subjectwood identification
dc.subjectXyloTron
dc.titleTowards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoodsen
dc.typeArtigo
unesp.author.orcid0000-0001-7240-7713[1]
unesp.author.orcid0000-0002-5421-3269[3]
unesp.author.orcid0000-0002-7053-8565[5]

Arquivos

Coleções

Artigos - Botânica - IBB