IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8280706.html
   My bibliography  Save this article

The Monitoring and Early Warning System of Water Biological Environment Based on Machine Vision

Author

Listed:
  • Lihong Zhou
  • Wenlong Hang

Abstract

Water contaminated by microorganisms can lead to the outbreak and prevalence of various diseases, which seriously threaten the health of people. In the monitoring of the water biological environment, the traditional methods have low detection sensitivity and low efficiency, so it is urgent to design a water biological monitoring system with low cost and high monitoring efficiency. Machine vision has the advantages of fast speed, appropriate precision, and strong anti-interference ability, which has been greatly developed in recent years. In this paper, the monitoring and early warning system of the water biological environment is built, in which the SVM algorithm is applied to image processing and feature extraction, and each module of the system is designed. Finally, the computational complexity of the system algorithm and the detection accuracy of the system are tested, and the results show that the system has the advantages of low cost, low computational complexity, and high monitoring efficiency, which can provide a reference for water resources protection.

Suggested Citation

  • Lihong Zhou & Wenlong Hang, 2022. "The Monitoring and Early Warning System of Water Biological Environment Based on Machine Vision," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-7, June.
    • Handle: RePEc:hin:jnlmpe:8280706
    DOI: 10.1155/2022/8280706
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/8280706.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/8280706.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/8280706?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:8280706. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.