Literature DB >> 33910606

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

Sung-Wook Hwang1, Junji Sugiyama2,3.   

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.

Entities:  

Keywords:  Computer vision; Convolutional neural networks; Deep learning; Image recognition; Machine learning; Wood anatomy; Wood identification

Year:  2021        PMID: 33910606     DOI: 10.1186/s13007-021-00746-1

Source DB:  PubMed          Journal:  Plant Methods        ISSN: 1746-4811            Impact factor:   4.993


  16 in total

1.  Metabolic chemotypes of CITES protected Dalbergia timbers from Africa, Madagascar, and Asia.

Authors:  Pamela J McClure; Gabriela D Chavarria; Edgard Espinoza
Journal:  Rapid Commun Mass Spectrom       Date:  2015-05-15       Impact factor: 2.419

2.  Control of origin of larch wood: discrimination between European (Austrian) and Siberian origin by stable isotope analysis.

Authors:  Micha Horacek; Michael Jakusch; Hannes Krehan
Journal:  Rapid Commun Mass Spectrom       Date:  2009-12       Impact factor: 2.419

3.  Evaluation of candidate DNA barcoding loci for economically important timber species of the mahogany family (Meliaceae).

Authors:  A N Muellner; H Schaefer; R Lahaye
Journal:  Mol Ecol Resour       Date:  2011-02-06       Impact factor: 7.090

Review 4.  Machine learning: Trends, perspectives, and prospects.

Authors:  M I Jordan; T M Mitchell
Journal:  Science       Date:  2015-07-17       Impact factor: 47.728

5.  Digitization of herbaria enables novel research.

Authors:  Pamela S Soltis
Journal:  Am J Bot       Date:  2017-09       Impact factor: 3.844

6.  Deep learning in biomedicine.

Authors:  Michael Wainberg; Daniele Merico; Andrew Delong; Brendan J Frey
Journal:  Nat Biotechnol       Date:  2018-09-06       Impact factor: 54.908

7.  Fake legal logging in the Brazilian Amazon.

Authors:  Pedro H S Brancalion; Danilo R A de Almeida; Edson Vidal; Paulo G Molin; Vanessa E Sontag; Saulo E X F Souza; Mark D Schulze
Journal:  Sci Adv       Date:  2018-08-15       Impact factor: 14.136

8.  Anatomical features of Fagaceae wood statistically extracted by computer vision approaches: Some relationships with evolution.

Authors:  Kayoko Kobayashi; Takahiro Kegasa; Sung-Wook Hwang; Junji Sugiyama
Journal:  PLoS One       Date:  2019-08-12       Impact factor: 3.240

9.  Wood recognition using image texture features.

Authors:  Hang-jun Wang; Guang-qun Zhang; Heng-nian Qi
Journal:  PLoS One       Date:  2013-10-11       Impact factor: 3.240

10.  Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks.

Authors:  Prabu Ravindran; Adriana Costa; Richard Soares; Alex C Wiedenhoeft
Journal:  Plant Methods       Date:  2018-03-23       Impact factor: 4.993

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  1 in total

1.  Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods.

Authors:  Prabu Ravindran; Frank C Owens; Adam C Wade; Rubin Shmulsky; Alex C Wiedenhoeft
Journal:  Front Plant Sci       Date:  2022-01-21       Impact factor: 5.753

  1 in total

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