Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni

Abstract

Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers—Decision Tree C5.0, Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers.

Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

“…Wood anatomical research is a field where DCNNs find an ideal application ( Garcia-Pedrero et al, 2019 ). In the past, machine learning methods have mainly been used for wood species identification ( Luis et al, 2009 ; Mallik et al, 2011 ; Ravindran et al, 2018 ; He et al, 2020 ; Wu et al, 2021 ). In contrast, quantitative wood anatomy (QWA), that refers to the broad set of analyses quantifying and interpreting the variation of xylem features in trees, shrubs, and herbaceous plants ( von Arx et al, 2016 ), has just started being investigated with such tools.…”

WorldForestID: Addressing the need for standardized wood reference collections to support authentication analysis technologies; a way forward for checking the origin and identity of traded timber

“…(2020). Attempts to reduce the need for human expertise are being made using automated machine learning (Andrade et al., 2020; He, et al., 2020; He et al., 2020; Hermanson et al., 2019; Hermanson & Wiedenhoeft, 2011; Ibrahim et al., 2017; Olschofsky & Kohl, 2020; Paula Filho et al., 2014; Ravindran et al., 2020; Souza et al., 2020; Wang et al., 2013; Yusof et al., 2013). This is clearly a rapidly moving field of study.…”

The applications of machine vision in raw material and production of wood products

“…The traditional wood identification is based on wood anatomy, which requires comprehensive judgment of wood macroscopic characteristics and microstructure. The task is arduous due to the diversity of tree species and the need for professional wood knowledge reserve, while classification capacity is often limited to the "genera" or "classes" of wood (He et al 2020b;Hwang and Sugiyama 2021). In addition, the characteristics exhibited by different species of wood need to be extracted accurately for subsequent classification.…”