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.

我们开发了一种用于系统生成大型显微纤维材料的图像数据集的方法，基于此方法，我们生成了用于九种硬木属的图像数据。这是首次通过深度学习自动化识别显微图像中的硬木物种的实质性方法的基础。我们的方法包括一种灵活的管道，用于容易地注释管胞要素。我们比较了不同的神经网络架构和超参数的性能。我们提出的方法与人类专家的表现相似。这将来将改善对全球木纤维产品流动的控制，以保护森林。