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

TL;DR

This paper presents a deep learning-based method for automating the identification of hardwood species in microscopic images of fibrous materials, utilizing a large dataset and flexible annotation pipeline.

Contribution

It introduces a novel methodology for generating large wood image datasets and demonstrates automated species identification comparable to human experts.

Findings

Deep learning models achieve accuracy similar to human experts.

A flexible annotation pipeline simplifies vessel element labeling.

The approach enables scalable, automated wood species classification.

Abstract

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.