The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products
dc.contributor.author | Ravindran, Prabu | |
dc.contributor.author | Thompson, Blaise J. | |
dc.contributor.author | Soares, Richard K. | |
dc.contributor.author | Wiedenhoeft, Alex C. | |
dc.contributor.institution | USDA | |
dc.contributor.institution | Univ Wisconsin | |
dc.contributor.institution | Purdue Univ | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2020-12-10T17:38:53Z | |
dc.date.available | 2020-12-10T17:38:53Z | |
dc.date.issued | 2020-07-10 | |
dc.description.abstract | Forests, 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.affiliation | USDA, Ctr Wood Anat Res, Forest Prod Lab, Madison, WI 53726 USA | |
dc.description.affiliation | Univ Wisconsin, Dept Bot, Madison, WI 53705 USA | |
dc.description.affiliation | Univ Wisconsin, Dept Chem, 1101 Univ Ave, Madison, WI 53706 USA | |
dc.description.affiliation | Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA | |
dc.description.affiliation | Univ Estadual Paulista, Dept Ciencias Biol Bot, Botucatu, SP, Brazil | |
dc.description.affiliationUnesp | Univ Estadual Paulista, Dept Ciencias Biol Bot, Botucatu, SP, Brazil | |
dc.description.sponsorship | US Department of State | |
dc.description.sponsorship | Forest Stewardship Council | |
dc.description.sponsorship | Wisconsin Idea Baldwin Grant | |
dc.description.sponsorshipId | US Department of State: 19318814Y0010 | |
dc.format.extent | 8 | |
dc.identifier | http://dx.doi.org/10.3389/fpls.2020.01015 | |
dc.identifier.citation | Frontiers In Plant Science. Lausanne: Frontiers Media Sa, v. 11, 8 p., 2020. | |
dc.identifier.doi | 10.3389/fpls.2020.01015 | |
dc.identifier.issn | 1664-462X | |
dc.identifier.uri | http://hdl.handle.net/11449/195564 | |
dc.identifier.wos | WOS:000555887100001 | |
dc.language.iso | eng | |
dc.publisher | Frontiers Media Sa | |
dc.relation.ispartof | Frontiers In Plant Science | |
dc.source | Web of Science | |
dc.subject | wood identification | |
dc.subject | charcoal identification | |
dc.subject | convolutional neural networks | |
dc.subject | deep learning | |
dc.subject | sustainability | |
dc.subject | forest products | |
dc.subject | computer vision | |
dc.title | The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products | en |
dc.type | Artigo | |
dcterms.rightsHolder | Frontiers Media Sa |