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Developing deep learning models to automate rosewood tree species identification for CITES designation and implementation

  • Tuo He , Yang Lu , Lichao Jiao , Yonggang Zhang , Xiaomei Jiang and Yafang Yin EMAIL logo
From the journal Holzforschung


The implementation of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) to combat illegal logging and associated trade necessitates accurate and efficient field screening of wood species. In this study, a total of 10,237 images of 15 Dalbergia and 11 Pterocarpus species were collected from the transverse surfaces of 417 wood specimens. Three deep learning models were then constructed, trained, and tested with these images to discriminate between timber species. The optimal parameters of the deep learning model were analyzed, and the representative wood anatomical features that were activated by the deep learning models were visualized. The results demonstrated that the overall accuracies of the 26-class, 15-class, and 11-class models were 99.3, 93.7, and 88.4%, respectively. It is suggested that at least 100 high-quality images per species with minimum patch sizes of 1000 × 1000 from more than 10 wood specimens were needed to train reliable and applicable deep learning models. The feature visualization indicated that the vessel groupings and axial parenchyma were the main wood anatomical features activated by the deep learning models. The combination of the state-of-the-art deep learning models, parameter configuration, and feature visualization provide a time- and cost-effective tool for the field screening of wood species to support effective CITES designation and implementation.

Corresponding author: Yafang Yin, Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China; and Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China, E-mail:

Award Identifier / Grant number: CAFYBB2017ZE003

Funding source: National Special Support Plan of China

Award Identifier / Grant number: W02020331


We would like to acknowledge Mr. Zhiyuan Zou for his assistance to model development, Dr. Prabu Ravindran, Dr. Richard Soares and Dr. Alex C. Wiedenhoeft of Forest Products Laboratory, USDA for their contributions to image collection and data curation. We also would like to acknowledge Dr. Bo Liu, Dr. Juan Guo, Dr. Shan Li of Chinese Academy of Forestry, Dr. Alexandre Bahia Gontijoa of Forest Products Laboratory of Brazil and Dr. Volker Haag of Thünen Institute of Germany for their assistance with the wood image identification tests.

  1. Author contributions: Tuo He: conceptualization, data curation, investigation, methodology, software, visualization, writing (original draft); Yang Lu: data curation, investigation, validation; Lichao Jiao: formal analysis, resources; Yonggang Zhang: data curation, resources; Xiaomei Jiang: investigation; Yafang Yin: funding acquisition, supervision, writing-review and editing.

  2. Research funding: This work was supported by the Project of Chinese Academy of Forestry (CAFYBB2017ZE003) and National Special Support Plan of China (no. W02020331).

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.


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Received: 2020-01-11
Accepted: 2020-03-19
Published Online: 2020-06-01
Published in Print: 2020-11-18

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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