Caveat emptor: On the Need for Baseline Quality Standards in Computer Vision Wood Identification

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dc.contributor.authorRavindran, Prabu
dc.contributor.authorWiedenhoeft, Alex C. [UNESP]
dc.contributor.institutionUniversity of Wisconsin
dc.contributor.institutionForest Products Laboratory
dc.contributor.institutionPurdue University
dc.contributor.institutionMississippi State University
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-03-01T20:37:44Z
dc.date.available2023-03-01T20:37:44Z
dc.date.issued2022-04-01
dc.description.abstractComputer vision wood identification (CVWID) has focused on laboratory studies reporting consistently high model accuracies with greatly varying input data quality, data hygiene, and wood identification expertise. Employing examples from published literature, we demonstrate that the highly optimistic model performance in prior works may be attributed to evaluating the wrong functionality—wood specimen identification rather than the desired wood species or genus identification—using limited datasets with data hygiene practices that violate the requirement of clear separation between training and evaluation data. Given the lack of a rigorous framework for a valid methodology and its objective evaluation, we present a set of minimal baseline quality standards for performing and reporting CVWID research and development that can enable valid, objective, and fair evaluation of current and future developments in this rapidly developing field. To elucidate the quality standards, we present a critical revisitation of a prior CVWID study of North American ring-porous woods and an exemplar study incorporating best practices on a new dataset covering the same set of woods. The proposed baseline quality standards can help translate models with high in silico performance to field-operational CVWID systems and allow stakeholders in research, industry, and government to make informed, evidence‐based modality‐agnostic decisions.en
dc.description.affiliationDepartment of Botany University of Wisconsin
dc.description.affiliationCenter for Wood Anatomy Research USDA Forest Service Forest Products Laboratory
dc.description.affiliationDepartment of Forestry and Natural Resources Purdue University
dc.description.affiliationDepartment of Sustainable Bioproducts Mississippi State University
dc.description.affiliationDepartamento de Ciências Biológicas (Botânica) Universidade Estadual Paulista Botucatu, SP
dc.description.affiliationUnespDepartamento de Ciências Biológicas (Botânica) Universidade Estadual Paulista Botucatu, SP
dc.identifierhttp://dx.doi.org/10.3390/f13040632
dc.identifier.citationForests, v. 13, n. 4, 2022.
dc.identifier.doi10.3390/f13040632
dc.identifier.issn1999-4907
dc.identifier.scopus2-s2.0-85129168788
dc.identifier.urihttp://hdl.handle.net/11449/240903
dc.language.isoeng
dc.relation.ispartofForests
dc.sourceScopus
dc.subjectbest practices
dc.subjectcomputer vision
dc.subjectmachine learning
dc.subjectwood identification
dc.subjectXyloTron
dc.titleCaveat emptor: On the Need for Baseline Quality Standards in Computer Vision Wood Identificationen
dc.typeArtigo

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