Computer-assisted timber identification based on features extracted from microscopic wood sections

What is it about?

Wood anatomy is one of the most important methods for timber identification. The aim of our study is to review existing computer vision methods and compare the success of species identification based on more traditional and state-of-the-art approaches for image classification. We find an accuracy rate up to 95.6% based on a publicly available database including microscopic images from wood sections belonging to 112 species. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification, and motivates us to expand the existing database to a more extensive, worldwide reference database from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging, and would boost the societal value of wood anatomy along with its collections and experts.

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