Machine learning-based wood anatomy identification: towards anatomical feature recognition

IAWA Journal - KU Leuven/IAWA Journal, vol. 45, pp. 457–475

Abstract

Summary 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.

Authors 6

1. Xin He

   Southwest Forestry University

   Affiliation as printed

   Southwest Forestry University, Kunming, P.R. China

2. Daniël M. Pelt Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands

3. Jingran Gao

   Southwest Forestry University

   Affiliation as printed

   Southwest Forestry University, Kunming, P.R. China

4. Barbara Gravendeel Aachen Naturalis Biodiversity Center

   Naturalis Biodiversity Center

   Affiliation as printed

   Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA Leiden, The Netherlands

5. Jian Qiu

   Southwest Forestry University

   Affiliation as printed

   Southwest Forestry University, Kunming, P.R. China

6. Frederic Lens Aachen Institute of Biology Leiden Naturalis Biodiversity Center

   Leiden University · Naturalis Biodiversity Center

   Affiliation as printed

   Leiden University, Institute of Biology Leiden, Plant Sciences, Sylviusweg 72, 2333 BE Leiden, The Netherlands

   Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA Leiden, The Netherlands