Publication Date:
2024-05-02
Description:
Computer vision-based wood identification has been successfully applied to recognize tree species using digital
images of wood sections or surfaces. However, this image-to-species approach can only recognize a limited number of species
due to two main reasons: 1) the lack of a good reference database requiring high-quality standardized images from multiple
individuals of hundreds or even thousands of traded timber species, and 2) species not included in the reference database
cannot be identified without expert knowledge. Another bottleneck is that the feature extraction process used by these species
recognition approaches is a black box, thereby creating a discrepancy between machine learning features and wood anatomical
features. This discrepancy prevents wood anatomists from understanding how these machine-learning algorithms work. Here,
we survey currently existing methods used in feature extraction, classification, and deep learning methods applied in wood
identification along with their pitfalls and opportunities. As an example of how the field could move forward, we launch the
idea of building an image-to-features-to-species identification approach based on microscopic wood images as well as text files
comprising wood anatomical descriptions. If we can manage machine learning-based algorithms to recognize the main wood
anatomical traits that experts use to identify species in a (semi-)automated way, this would boost wood identification in two
ways: (1) extensive reference databases for each species would become less crucial as the databases are ordered at the trait level,
(2) timber identification would become more feasible for species that have not yet been included in the reference database as
long as wood anatomical descriptions are available.
Keywords:
feature incompatibility
;
illegal logging
;
species recognition
Repository Name:
National Museum of Natural History, Netherlands
Type:
info:eu-repo/semantics/article
Format:
application/pdf
