IDEAS home Printed from https://ideas.repec.org/a/caa/jnljfs/v67y2021i5id10-2020-jfs.html
   My bibliography  Save this article

Improving the quality of sorting wood chips by scanning and machine vision technology

Author

Listed:
  • Igor Grigorev

    (Department of Technology and Equipment of Forest Complex, Yakut State Agricultural Academy, Yakutsk, Russian Federation)

  • Anatoly Shadrin

    (Department of Technology and Equipment for Forestry Production, Bauman University - National Technological University (Mytishchi Branch), Mytishchi, Russian Federation)

  • Sergey Katkov

    (Department of Technology and Equipment for Forestry Production, Bauman University - National Technological University (Mytishchi Branch), Mytishchi, Russian Federation)

  • Vyacheslav Borisov

    (Department of Technology and Equipment for Forestry Production, Bauman University - National Technological University (Mytishchi Branch), Mytishchi, Russian Federation)

  • Varvara Druzyanova

    (Department of Operation of Road Transport and Auto Repair, Northeastern Federal University Named After MK Ammosov, Yakutsk, Russian Federation
    Department of Technological Systems in the Agricultural Sector, Yakut State Agricultural Academy, Yakutsk, Russian Federation)

  • Irina Gnatovskaya

    (Department of Technologies and Equipment of Timber Production, Bauman University - National Technological University, Mytishchi, Russian Federation)

  • Roman Diev

    (Department of Technologies and Equipment of Timber Production, Bauman University - National Technological University, Mytishchi, Russian Federation)

  • Natalya Kaznacheeva

    (Department of Technology and Equipment for Forestry Production, Bauman University - National Technological University (Mytishchi Branch), Mytishchi, Russian Federation)

  • Dmitry Levushkin

    (Department of Technology and Equipment for Forestry Production, Bauman University - National Technological University (Mytishchi Branch), Mytishchi, Russian Federation)

  • Dmitriy Akinin

    (Department of Transport and Technological Means and Equipment of the Forest Complex, Bauman University - National Technological University (Mytishchi Branch), Mytishchi, Russian Federation)

Abstract

Improving the quality of sorting wood waste is the main problem in the timber industry from the point of view of saving energy resources and preserving the environment, associated with the intensity of forest harvesting. Depending on the required quality characteristics, the sorting of wood chips makes it possible to determine their further use in production or utilization as a fuel. This paper presents the results of the development of a novel approach to sorting wood chips on a conveyor belt using machine learning and scanning technology. The proposed methodology includes functions to analyze the fractional size distribution among wood chips and rot detection. It shows that once a defective unit is detected, the quality control system will automatically remove it from the conveyor belt while it is moving. The minimization of wood waste will reduce logging intensity and increase the profitability of lumber enterprises.

Suggested Citation

  • Igor Grigorev & Anatoly Shadrin & Sergey Katkov & Vyacheslav Borisov & Varvara Druzyanova & Irina Gnatovskaya & Roman Diev & Natalya Kaznacheeva & Dmitry Levushkin & Dmitriy Akinin, 2021. "Improving the quality of sorting wood chips by scanning and machine vision technology," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(5), pages 212-218.
    • Handle: RePEc:caa:jnljfs:v:67:y:2021:i:5:id:10-2020-jfs
    DOI: 10.17221/10/2020-JFS
    as

    Download full text from publisher

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/10/2020-JFS.html
    Download Restriction: free of charge
    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/10/2020-JFS.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/10/2020-JFS?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rahman, Abdur & Marufuzzaman, Mohammad & Street, Jason & Wooten, James & Gude, Veera Gnaneswar & Buchanan, Randy & Wang, Haifeng, 2024. "A comprehensive review on wood chip moisture content assessment and prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnljfs:v:67:y:2021:i:5:id:10-2020-jfs. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.