The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products

dc.contributor.authorRavindran, Prabu
dc.contributor.authorThompson, Blaise J.
dc.contributor.authorSoares, Richard K.
dc.contributor.authorWiedenhoeft, Alex C.
dc.contributor.institutionUSDA
dc.contributor.institutionUniv Wisconsin
dc.contributor.institutionPurdue Univ
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-10T17:38:53Z
dc.date.available2020-12-10T17:38:53Z
dc.date.issued2020-07-10
dc.description.abstractForests, estimated to contain two thirds of the world's biodiversity, face existential threats due to illegal logging and land conversion. Efforts to combat illegal logging and to support sustainable value chains are hampered by a critical lack of affordable and scalable technologies for field-level inspection of wood and wood products. To meet this need we present the XyloTron, a complete, self-contained, multi-illumination, field-deployable, open-source platform for field imaging and identification of forest products at the macroscopic scale. The XyloTron platform integrates an imaging system built with off-the-shelf components, flexible illumination options with visible and UV light sources, software for camera control, and deep learning models for identification. We demonstrate the capabilities of the XyloTron platform with example applications for automatic wood and charcoal identification using visible light and human-mediated wood identification based on ultra-violet illumination and discuss applications in field imaging, metrology, and material characterization of other substrates.en
dc.description.affiliationUSDA, Ctr Wood Anat Res, Forest Prod Lab, Madison, WI 53726 USA
dc.description.affiliationUniv Wisconsin, Dept Bot, Madison, WI 53705 USA
dc.description.affiliationUniv Wisconsin, Dept Chem, 1101 Univ Ave, Madison, WI 53706 USA
dc.description.affiliationPurdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA
dc.description.affiliationUniv Estadual Paulista, Dept Ciencias Biol Bot, Botucatu, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Ciencias Biol Bot, Botucatu, SP, Brazil
dc.description.sponsorshipUS Department of State
dc.description.sponsorshipForest Stewardship Council
dc.description.sponsorshipWisconsin Idea Baldwin Grant
dc.description.sponsorshipIdUS Department of State: 19318814Y0010
dc.format.extent8
dc.identifierhttp://dx.doi.org/10.3389/fpls.2020.01015
dc.identifier.citationFrontiers In Plant Science. Lausanne: Frontiers Media Sa, v. 11, 8 p., 2020.
dc.identifier.doi10.3389/fpls.2020.01015
dc.identifier.issn1664-462X
dc.identifier.urihttp://hdl.handle.net/11449/195564
dc.identifier.wosWOS:000555887100001
dc.language.isoeng
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers In Plant Science
dc.sourceWeb of Science
dc.subjectwood identification
dc.subjectcharcoal identification
dc.subjectconvolutional neural networks
dc.subjectdeep learning
dc.subjectsustainability
dc.subjectforest products
dc.subjectcomputer vision
dc.titleThe XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Productsen
dc.typeArtigo
dcterms.rightsHolderFrontiers Media Sa

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