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    Author(s): Prabu Ravindran; Alex C. Wiedenhoeft
    Date: 2020
    Source: Wood Science and Technology. 46(2): 1163. 12 p.
    Publication Series: Scientific Journal (JRNL)
    Station: Forest Products Laboratory
    PDF: Download Publication  (803.0 KB)

    Description

    A wealth of forensic wood identifcation technologies has been developed or improved in recent years, with many attempts to compare results between technologies. The utility of such comparisons is greatly reduced when the species tested with each technology are diferent and when performance metrics are not calculated or presented in the same way. Here, a species-level XyloTron computer vision model is presented along with a side-by-side comparison for species- and genus-level identifcation of the 10 species of Meliaceae studied by Deklerck et al. using mass spectrometry. The species-level accuracies of the XyloTron model and the mass spectrometry models are comparable, while the genus-level accuracy of the XyloTron model is higher than that of the mass spectrometry model. The paper concludes with a call for better practices to compare disparate forensic wood identifcation technologies from a performance driven perspective.

    Publication Notes

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Ravindran, Prabu; Wiedenhoeft, Alex C. 2020. Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry. Wood Science and Technology. 46(2): 1163. 12 p.

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    Keywords

    Forensic wood identification, computer vision, mass spectrometry, XyloTron, meliaceae, deep learning

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https://www.fs.usda.gov/treesearch/pubs/60851