IDEAS home Printed from https://ideas.repec.org/a/ids/ijarge/v16y2020i2p123-156.html
   My bibliography  Save this article

Classification of wheat seeds using image processing and fuzzy clustered random forest

Author

Listed:
  • Parminder Singh
  • Anand Nayyar
  • Simranjeet Singh
  • Avinash Kaur

Abstract

A reliable and autonomic seed classification technique can overcome the issues of manual seed classification. It is a highly practical and economically vital need of the agriculture industry. The current techniques of machine learning and artificial intelligence allows the researchers to design a new data mining mechanism with higher accuracy. In this article, a new adaptive technique has been proposed using a digital image processing system (DIPS) and fuzzy clustered random forest (FCRF) techniques. The DIPS is used to extract the parameters such as area, perimeter, height, width, length of the groove and asymmetry coefficient. Further, FCRF model is applied to classify the wheat seeds based on these parameters in a time-efficient manner. The devised approach helps the agriculture industry for seed classification, separation of damaged seeds and controlling the quality of seeds based on grading policy. The experiment result demonstrates that the accuracy of the proposed technique is better than the existing wheat seed classification algorithm. The average performance gain of the proposed technique is up to 97.7%.

Suggested Citation

  • Parminder Singh & Anand Nayyar & Simranjeet Singh & Avinash Kaur, 2020. "Classification of wheat seeds using image processing and fuzzy clustered random forest," International Journal of Agricultural Resources, Governance and Ecology, Inderscience Enterprises Ltd, vol. 16(2), pages 123-156.
    • Handle: RePEc:ids:ijarge:v:16:y:2020:i:2:p:123-156
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=109048
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijarge:v:16:y:2020:i:2:p:123-156. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=1 .

    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.