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We have developed a methodology for the systematic generation of a largeimage dataset of macerated wood references, which we used to generate imagedata for nine hardwood genera. This is the basis for a substantial approach toautomate, for the first time, the identification of hardwood species inmicroscopic images of fibrous materials by deep learning. Our methodologyincludes a flexible pipeline for easy annotation of vessel elements. We comparethe 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 protectforests.

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning