IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i1d10.1007_s13198-022-01814-y.html
   My bibliography  Save this article

A novel edge detection method for medicinal plant's leaf features extraction

Author

Listed:
  • Jibi G. Thanikkal

    (Amity University Uttar Pradesh)

  • Ashwani Kumar Dubey

    (Amity University Uttar Pradesh)

  • M. T. Thomas

    (St. Thomas College)

Abstract

Morphological features-based leaf identification algorithms provide highly accurate results. But it is required a single-lined edge extraction algorithm for morphological feature generation. Existing edge extraction algorithms have heavy calculations and higher iteration steps to extract edges. The simplicity of the edge detection algorithm helps to reduce the complexity of the image feature extraction process. In this paper, a fast and straightforward novel edge detection algorithm is introduced in the spatial domain. In a single iteration over all the pixels of the image, our algorithm can achieve a better result than existing edge detection techniques. Also, this paper provides a novel algorithm for leaf shape, vein, apex, and base feature extraction techniques using the edge detection algorithm that can be utilized further for the classification and identification of medicinal plant species or any other plant species too. The performance measure of the proposed edge detection algorithm for leaf features is better as compared to the existing edge detection algorithms. This edge detection algorithm achieved 92% of accuracy and a PSNR rate of 10.88 dB with the time complexity of O(n*m), where n is the height and m is the width of the given image. The importance of medicinal plant identification and existing leaf identification techniques are also discussed in this paper.

Suggested Citation

  • Jibi G. Thanikkal & Ashwani Kumar Dubey & M. T. Thomas, 2023. "A novel edge detection method for medicinal plant's leaf features extraction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 448-458, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01814-y
    DOI: 10.1007/s13198-022-01814-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01814-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-022-01814-y?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.

    References listed on IDEAS

    as
    1. Fangsheng Wu & Changan Zhu & Jinxiu Xu & Mohammed Wasim Bhatt & Ashutosh Sharma, 2022. "Research on image text recognition based on canny edge detection algorithm and k-means algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 72-80, March.
    2. Thai Leang Sung & Hyo Jong Lee, 2020. "Depth edge detection using edge-preserving filter and morphological operations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 812-817, August.
    3. Ruchika Singh & Munish Vashishath & S. Kumar, 2019. "Ant colony optimization technique for edge detection using fuzzy triangular membership function," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(1), pages 91-96, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weiwei Sun & Huiqian Wang & Yi Lu & Jiasai Luo & Ting Liu & Jinzhao Lin & Yu Pang & Guo Zhang, 2022. "Deep-Learning-Based Complex Scene Text Detection Algorithm for Architectural Images," Mathematics, MDPI, vol. 10(20), pages 1-22, October.

    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:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01814-y. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.