Next Article in Journal
Improved Cellular Automaton for Stand Delineation
Previous Article in Journal
Foliar and Wood Traits Covary along a Vertical Gradient within the Crown of Long-Lived Light-Demanding Species of the Congo Basin Semi-Deciduous Forest
Open AccessArticle

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

by Tuo He 1,2,3, João Marco 3, Richard Soares 3,4, Yafang Yin 1,2 and Alex C. Wiedenhoeft 3,4,5,6,*
1
Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
2
Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China
3
Center for Wood Anatomy Research, USDA Forest Service, Forest Products Laboratory, Madison, WI 53726, USA
4
Department of Botany, University of Wisconsin, Madison, WI 53706, USA
5
Department of Forestry and National Resources, Purdue University, West Lafayette, IN 47907, USA
6
Ciências Biológicas (Botânica), Universidade Estadual Paulista, Botucatu 18610-034, São Paulo, Brazil
*
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 36; https://doi.org/10.3390/f11010036
Received: 3 November 2019 / Revised: 13 December 2019 / Accepted: 21 December 2019 / Published: 25 December 2019
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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. View Full-Text
Keywords: CITES; machine learning; quantitative wood anatomy; SVM; Swietenia CITES; machine learning; quantitative wood anatomy; SVM; Swietenia
Show Figures

Figure 1

MDPI and ACS Style

He, T.; Marco, J.; Soares, R.; Yin, Y.; Wiedenhoeft, A.C. Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni. Forests 2020, 11, 36.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop