mlsanityData to 14 Oct 2026

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

arXiv 2307.09588 ↗First posted 18 Jul 2023cs.CV cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 18 Jul 2023.

Builds on

20 of 47 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestrycited by 2
  2. 2024Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigationcited by 0
  3. 2024WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Imagescited by 0