IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i10p1543-d924333.html
   My bibliography  Save this article

A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet

Author

Listed:
  • Changguang Feng

    (College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • Minlan Jiang

    (College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • Qi Huang

    (College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • Lingguo Zeng

    (College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • Changjiang Zhang

    (School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China)

  • Yulong Fan

    (Zhejiang Dahua Technology Co., Ltd., Hangzhou 310053, China)

Abstract

The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. To address the above problems, this paper proposes a real-time rice blast disease segmentation method based on a feature fusion and attention mechanism: Deep Feature Fusion and Attention Network (abbreviated to DFFANet). To realize the extraction of the shallow and deep features of rice blast disease as complete as possible, a feature extraction (DCABlock) module and a feature fusion (FFM) module are designed; then, a lightweight attention module is further designed to guide the features learning, effectively fusing the extracted features at different scales, and use the above modules to build a DFFANet lightweight network model. This model is applied to rice blast spot segmentation and compared with other existing methods in this field. The experimental results show that the method proposed in this study has better anti-interference ability, achieving 96.15% MioU, a speed of 188 FPS, and the number of parameters is only 1.4 M, which can achieve a high detection speed with a small number of model parameters, and achieves an effective balance between segmentation accuracy and speed, thereby reducing the requirements for hardware equipment and realizing low-cost embedded development. It provides technical support for real-time rapid detection of rice diseases.

Suggested Citation

  • Changguang Feng & Minlan Jiang & Qi Huang & Lingguo Zeng & Changjiang Zhang & Yulong Fan, 2022. "A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet," Agriculture, MDPI, vol. 12(10), pages 1-12, September.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1543-:d:924333
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/10/1543/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/10/1543/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shuo Chen & Kefei Zhang & Yindi Zhao & Yaqin Sun & Wei Ban & Yu Chen & Huifu Zhuang & Xuewei Zhang & Jinxiang Liu & Tao Yang, 2021. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation," Agriculture, MDPI, vol. 11(5), pages 1-18, May.
    2. Mingfeng Huang & Guoqin Xu & Junyu Li & Jianping Huang, 2021. "A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++," Agriculture, MDPI, vol. 11(12), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Cuiling Li & Xiu Wang & Liping Chen & Xueguan Zhao & Yang Li & Mingzhou Chen & Haowei Liu & Changyuan Zhai, 2023. "Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns," Agriculture, MDPI, vol. 13(9), pages 1-26, August.

    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. Mingfeng Huang & Guoqin Xu & Junyu Li & Jianping Huang, 2021. "A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++," Agriculture, MDPI, vol. 11(12), pages 1-14, December.
    2. Rutuja Rajendra Patil & Sumit Kumar & Shwetambari Chiwhane & Ruchi Rani & Sanjeev Kumar Pippal, 2022. "An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    3. Md. Mehedi Hasan & Touficur Rahman & A. F. M. Shahab Uddin & Syed Md. Galib & Mostafijur Rahman Akhond & Md. Jashim Uddin & Md. Alam Hossain, 2023. "Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration," Agriculture, MDPI, vol. 13(8), pages 1-17, August.

    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:gam:jagris:v:12:y:2022:i:10:p:1543-:d:924333. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.