Liu Shoujia, Zheng Chang, Wang Jiajun, Lu Yang, Yao Jie, Zou Zhiyuan, Yin Yafang, He Tuo
Department of Wood Anatomy and Utilization, Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, China.
Wood Collections, Chinese Academy of Forestry, Beijing, China.
Front Plant Sci. 2024 Jun 28;15:1368885. doi: 10.3389/fpls.2024.1368885. eCollection 2024.
Global 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.
This 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.
The 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.
This 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.
全球非法木材贸易是树种多样性丧失的主要原因。《濒危野生动植物种国际贸易公约》（CITES）旨在打击非法国际木材贸易。其实施依赖于用于现场筛查的准确木材识别技术。然而，对于非木材科学专业的执法人员来说，使用依赖木材解剖学的传统木材识别方法来满足木材现场物种水平筛查的需求既复杂又耗时，且具有挑战性。
本研究构建了一个CITES - 28宏观图像数据集，包括来自14种CITES列出的常见贸易树种和14种相似树种的279份木材标本的9437张原始图像。我们评估了一种合适的木材图像预处理方法，并在增强后的图像数据集上开发了一种高效的计算机视觉分类模型SE - ResNet。该模型将注意力机制模块[挤压与激励网络（SENet）]纳入卷积神经网络（ResNet）以识别28种木材。
结果表明，SE - ResNet模型的准确率达到了显著的99.65%。此外，图像裁剪和旋转被证明是有效的图像预处理方法，可用于数据增强。本研究还使用木材市场新标本的图像进行实际识别以测试该模型，准确率达到了82.3%。
本研究提出了一种结合SENet模块的卷积神经网络模型，用于区分CITES列出的物种与其相似物种，并研究了增强木材横向图像数据的标准指南，提供了一种实用的计算机视觉方法工具来保护濒危树种，并突出了其在CITES实施方面的巨大潜力。
