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Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Authors
  • Hwang, Sung-Wook1
  • Sugiyama, Junji1, 2
  • 1 Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan , Kyoto (Japan)
  • 2 Nanjing Forestry University, Nanjing, 210037, China , Nanjing (China)
Type
Published Article
Journal
Plant Methods
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Apr 28, 2021
Volume
17
Issue
1
Identifiers
DOI: 10.1186/s13007-021-00746-1
Source
Springer Nature
Keywords
License
Green

Abstract

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

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