We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.

翻译：我们开发了一种系统生成浸渍木材参考大图像数据集的方法，并利用该方法生成了九个硬木属的图像数据。这为首次通过深度学习自动识别纤维材料显微图像中的硬木物种提供了实质性基础。我们的方法包括一个灵活的管道，用于轻松标注导管分子。我们比较了不同神经网络架构和超参数的性能。所提出的方法表现与人类专家相当。未来，这将改进对全球木纤维产品流动的管控，以保护森林。