Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
This work addresses the need for automated wood species detection to improve controls on global wood fiber product flows and protect forests, representing a domain-specific application.
The researchers tackled the problem of identifying hardwood species in microscopic images of fibrous materials by developing a methodology for generating a large image dataset of macerated wood references and using deep learning for automation. Their proposed method performs similarly well to human experts.
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.