Predicting Hardwood Porosity Domains: Toward Cascading Computer-Vision Wood Identification Models

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorMississippi State University-
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorForest Products Laboratory-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorOwens, Frank C.-
Autor(es): dc.creatorRavindran, Prabu-
Autor(es): dc.creatorCosta, Adriana-
Autor(es): dc.creatorShmulsky, Rubin-
Autor(es): dc.creatorWiedenhoeft, Alex C.-
Data de aceite: dc.date.accessioned2025-08-21T20:21:57Z-
Data de disponibilização: dc.date.available2025-08-21T20:21:57Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.15376/biores.19.4.9741-9772-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307920-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307920-
Descrição: dc.descriptionPrior work on computer-vision wood identification (CVWID) for North American hardwoods yielded two independent deep learning models – a 22-class model for diffuse-porous woods and a 17-class model for ring-porous woods – but did not address semi-ring-porous woods nor provide a CVWID solution for an unknown specimen without a human first determining which model to deploy. As untrained human operators would lack the anatomical proficiency to differentiate among porosity domains, it is necessary to develop a consolidated model that can identify diffuse-, ring-, and semi-ring-porous woods. Previous research suggests that prediction accuracy might decrease as class number grows. A potential strategy to reduce the number of classes a CVWID system must consider at a time is to hierarchically deploy a cascade of models. In pursuit of a unified model that can cover North American hardwoods of all porosity types, this study compared the accuracies of a consolidated 39-class (ring-+ diffuse-porous) model and a consolidated 42-class (ring-+ diffuse-+ semi-ring-porous) model with a two-tiered, cascading model scheme whereby images are first differentiated into three porosity domain classes and then again into only those taxonomic classes with that porosity. The results showed that the cascading model scheme can mitigate the accuracy reductions incurred by the 42-class model and nearly eliminate the occurrence of cross-domain misidentifications.-
Descrição: dc.descriptionDepartment of Sustainable Bioproducts Mississippi State University-
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.descriptionDepartamento de Ciências Biológicas (Botânica) Universidade Estadual Paulista – Botucatu-
Descrição: dc.descriptionDepartamento de Ciências Biológicas (Botânica) Universidade Estadual Paulista – Botucatu-
Formato: dc.format9741-9772-
Idioma: dc.languageen-
Relação: dc.relationBioResources-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCascading models-
Palavras-chave: dc.subjectComputer vision-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectPorosity domain-
Palavras-chave: dc.subjectWood identification-
Palavras-chave: dc.subjectXyloTron-
Título: dc.titlePredicting Hardwood Porosity Domains: Toward Cascading Computer-Vision Wood Identification Models-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.