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

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorForest Products Laboratory-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.contributorMississippi State University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRavindran, Prabu-
Autor(es): dc.creatorWiedenhoeft, Alex C.-
Data de aceite: dc.date.accessioned2025-08-21T23:08:54Z-
Data de disponibilização: dc.date.available2025-08-21T23:08:54Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/f13040632-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240903-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240903-
Descrição: dc.descriptionComputer 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.-
Descrição: dc.descriptionDepartment of Botany University of Wisconsin-
Descrição: dc.descriptionCenter for Wood Anatomy Research USDA Forest Service Forest Products Laboratory-
Descrição: dc.descriptionDepartment of Forestry and Natural Resources Purdue University-
Descrição: dc.descriptionDepartment of Sustainable Bioproducts Mississippi State University-
Descrição: dc.descriptionDepartamento de Ciências Biológicas (Botânica) Universidade Estadual Paulista Botucatu, SP-
Descrição: dc.descriptionDepartamento de Ciências Biológicas (Botânica) Universidade Estadual Paulista Botucatu, SP-
Idioma: dc.languageen-
Relação: dc.relationForests-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectbest practices-
Palavras-chave: dc.subjectcomputer vision-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectwood identification-
Palavras-chave: dc.subjectXyloTron-
Título: dc.titleCaveat emptor: On the Need for Baseline Quality Standards in Computer Vision Wood Identification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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