Automated identification of Lauraceae by scale-invariant feature transform

Abstract

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 mm2, 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 Cinnmamomum, 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.