Frontiers in Plant Science (Jun 2024)

How to discriminate wood of CITES-listed tree species from their look-alikes: using an attention mechanism with the ResNet model on an enhanced macroscopic image dataset

  • Shoujia Liu,
  • Shoujia Liu,
  • Chang Zheng,
  • Chang Zheng,
  • Jiajun Wang,
  • Jiajun Wang,
  • Jiajun Wang,
  • Yang Lu,
  • Yang Lu,
  • Jie Yao,
  • Zhiyuan Zou,
  • Yafang Yin,
  • Yafang Yin,
  • Tuo He,
  • Tuo He,
  • Tuo He

DOI
https://doi.org/10.3389/fpls.2024.1368885
Journal volume & issue
Vol. 15

Abstract

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IntroductionGlobal illegal trade in timbers is a major cause of the loss of tree species diversity. The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) has been developed to combat the illegal international timber trade. Its implementation relies on accurate wood identification techniques for field screening. However, meeting the demand for timber field screening at the species level using the traditional wood identification method depending on wood anatomy is complicated, time-consuming, and challenging for enforcement officials who did not major in wood science.MethodsThis study constructed a CITES-28 macroscopic image dataset, including 9,437 original images of 279 xylarium wood specimens from 14 CITES-listed commonly traded tree species and 14 look-alike species. We evaluated a suitable wood image preprocessing method and developed a highly effective computer vision classification model, SE-ResNet, on the enhanced image dataset. The model incorporated attention mechanism modules [squeeze-and-excitation networks (SENet)] into a convolutional neural network (ResNet) to identify 28 wood species.ResultsThe results showed that the SE-ResNet model achieved a remarkable 99.65% accuracy. Additionally, image cropping and rotation were proven effective image preprocessing methods for data enhancement. This study also conducted real-world identification using images of new specimens from the timber market to test the model and achieved 82.3% accuracy.ConclusionThis study presents a convolutional neural network model coupled with the SENet module to discriminate CITES-listed species with their look-alikes and investigates a standard guideline for enhancing wood transverse image data, providing a practical computer vision method tool to protect endangered tree species and highlighting its substantial potential for CITES implementation.

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