IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v6y2025i1d10.1007_s43069-024-00400-1.html
   My bibliography  Save this article

Genetic Algorithm–Aided Deep Feature Selection for Improved Rice Disease Classification

Author

Listed:
  • Rahul Sharma

    (GDC Marh)

  • Amar Singh

    (Lovely Professional University)

  • Prashant Kumar

    (Lovely Professional University)

  • Mahipal Singh

    (Lovely Professional University)

Abstract

Pests and diseases pose significant threats to crop safety and accessibility. Deep learning integration with traditional pest management fosters sustainable agriculture, minimizes chemical pesticide use, and facilitates early detection of pests and crop diseases through image and sensor data analysis. To ensure food security, reduce costs, and enhance overall production, computer vision techniques are essential for processing complex, high-dimensional real-world data. However, data dimensionality reduction is crucial for achieving accurate disease identification. Pre-trained models can extract valuable features from data. In this study, InceptionV3 and MobileNet V2 were utilized to extract comprehensive features from a small UCI rice leaf disease dataset. However, classifier performance using these features is uncertain due to factors such as domain mismatch, high-level data representation, model biases, dataset variability, and task complexity. To address these challenges, a rigorous evaluation of pre-trained feature suitability is necessary. A genetic algorithm–based feature selection (FS) approach was employed. FS streamlines data, reducing the information required for disease identification. An optimized ANN classifier, MobileNet-GA-ANN, achieved a 96.58% accuracy, outperforming other methods.

Suggested Citation

  • Rahul Sharma & Amar Singh & Prashant Kumar & Mahipal Singh, 2025. "Genetic Algorithm–Aided Deep Feature Selection for Improved Rice Disease Classification," SN Operations Research Forum, Springer, vol. 6(1), pages 1-24, March.
  • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-024-00400-1
    DOI: 10.1007/s43069-024-00400-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-024-00400-1
    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/s43069-024-00400-1?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. Lu Yang & Hongquan Jiang, 2021. "Weld defect classification in radiographic images using unified deep neural network with multi-level features," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 459-469, 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. Deyuan Ma & Ping Jiang & Leshi Shu & Zhaoliang Gong & Yilin Wang & Shaoning Geng, 2024. "Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 55-73, January.
    2. Zelin Zhi & Hongquan Jiang & Deyan Yang & Jianmin Gao & Quansheng Wang & Xiaoqiao Wang & Jingren Wang & Yongxiang Wu, 2023. "An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1895-1909, April.
    3. Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.

    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:snopef:v:6:y:2025:i:1:d:10.1007_s43069-024-00400-1. 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.