An Interpretable Approach to Wood Species Identification Based on Anatomical Features in Microscopic Images

Wood recognition plays a vital role in the trade and conservation of rare wood species. However, the computer vision-based methods classify the wood species by the features that are not used within the framework of wood anatomy, leading to results that are not interpretable. This study proposes a novel wood recognition method that detects anatomical structures such as vessels, wood rays, and parenchyma in wood microscopic images. These structures are quantified and mapped to the International Association of Wood Anatomists (IAWA) features, which are then used for species classification. Experimental results on 32 wood species demonstrate the effectiveness of the approach, achieving an accuracy of 94.1%, precision of 92.6%, recall of 93.3%, and an F1-score of 92.7%. In addition to its recognition performance, the method may offer interpretable IAWA-based classification criteria in wood science. These findings suggest that the method could serve as an anatomically interpretable framework for wood species identification, contributing to the regulation of the rare timber trade and supporting the conservation of endangered tree species.