The anatomical structure of wood is complex and contains considerable information about its specific species, physical properties, growth environment, and other factors. While conventional wood anatomy has been established by systematizing the xylem anatomical features, which enables wood identification generally up to genus level, it is difficult to describe all the information comprehensively. This study apply two computer vision approaches to optical micrographs: the scale-invariant feature transform algorithm and connected-component labelling. They extract the shape and pore size information, respectively, statistically from the whole micrographs. Both approaches enable the efficient detection of specific features of 18 species from the family Fagaceae. Although the methods ignore the positional information, which is important for the conventional wood anatomy, the simple information on the shape or size of the elements is enough to describe the species-specificity of wood. In addition, according to the dendrograms calculated from the numerical distances of the features, the closeness of some taxonomic groups is inconsistent with the types of porosity, which is one of the typical classification systems for wood anatomy, but consistent with the evolution based on molecular phylogeny; for example, ring-porous group Cerris and radial-porous group Ilex are nested in the same cluster. We analyse which part of the wood structure gave the taxon-specific information, indicating that the latewood zone of group Cerris is similar to the whole zone of group Ilex. Computer vision approaches provide statistical information that uncovers new aspects of wood anatomy that have been overlooked by conventional visual inspection.

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The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency-inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.

An image dataset of the cross-sectional optical micrographs of the Lauraceae species including 39 species in 11 genera, capturing at least one full annual ring, was investigated by scale-invariant feature transform (SIFT), a computer vision-based feature extraction algorithm. We found an image of 900 × 900-pixel size at a pixel resolution of ca. 3 µm, corresponding to the actual size of 2.65 × 2.65 mm 2 , as the minimum requirement for the image dataset in terms of the accuracy of the recognition at both the genus and species levels. Among the several classifiers investigated, the linear discriminant analysis (LDA) presented the best performance reaching a maximum of 89.4% in the genus with a species identification of approximately 96.3%. Cluster analysis of all the SIFT descriptors for each image yielded practical information regarding the descriptors; they recognize selectively the cell lumina, cell corners, vessels, and axial and ray parenchyma cells. Therefore, the difference between the genus or species levels was determined per the variation in the quantities of these computer-based properties. Another clustering approach, the hierarchal dendrogram, was applied to visualize the numerical distances between the genus and species. Interestingly, even Machilus and Phoebe, which are difficult to distinguish by conventional visual inspection, are quite distantly classified at the genus level. In contrast, some species in Cinnamomum, Machilus and Litsea were categorized into different subgroups rather than the original genus. Microscopic wood identification is found to be possible at the genus level; however, the numerical dataset of the morphological features has various overlapping clusters, causing the genus-level identification of the Lauraceae to be more difficult than species-level identification.