  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.
