Computer vision-based wood identification: a review

José Luís Silva*, Rui Bordalo, José Pissarra, Paloma de Palacios

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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Abstract

Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.

Original languageEnglish
Article number2041
Number of pages26
JournalForests
Volume13
Issue number12
DOIs
Publication statusPublished - 30 Nov 2022

Keywords

  • Computer vision
  • Convolutional neural networks
  • Deep learning
  • Illegal logging
  • Image recognition
  • Machine learning
  • Wood anatomy
  • Wood identification

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