IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8915-d867705.html
   My bibliography  Save this article

HLNet Model and Application in Crop Leaf Diseases Identification

Author

Listed:
  • Yanlei Xu

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Shuolin Kong

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Zongmei Gao

    (Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, Washington State University, Prosser, WA 99350, USA)

  • Qingyuan Chen

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Yubin Jiao

    (Changchun Institute of Engineering and Technology, Changchun 130117, China)

  • Chenxiao Li

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

Abstract

Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture.

Suggested Citation

  • Yanlei Xu & Shuolin Kong & Zongmei Gao & Qingyuan Chen & Yubin Jiao & Chenxiao Li, 2022. "HLNet Model and Application in Crop Leaf Diseases Identification," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8915-:d:867705
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8915/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8915/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anil Bhujel & Na-Eun Kim & Elanchezhian Arulmozhi & Jayanta Kumar Basak & Hyeon-Tae Kim, 2022. "A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    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. Bulent Tugrul & Elhoucine Elfatimi & Recep Eryigit, 2022. "Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review," Agriculture, MDPI, vol. 12(8), pages 1-21, 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. Taejoo Kim & Hyeongjun Kim & Kyeonghoon Baik & Yukyung Choi, 2022. "Instance-Aware Plant Disease Detection by Utilizing Saliency Map and Self-Supervised Pre-Training," Agriculture, MDPI, vol. 12(8), pages 1-16, July.
    2. Xianguo Ren & Haiqing Tian & Kai Zhao & Dapeng Li & Ziqing Xiao & Yang Yu & Fei Liu, 2022. "Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision," Agriculture, MDPI, vol. 12(10), pages 1-17, October.
    3. Zahid Ullah & Najah Alsubaie & Mona Jamjoom & Samah H. Alajmani & Farrukh Saleem, 2023. "EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images," Agriculture, MDPI, vol. 13(3), pages 1-13, March.
    4. Xiang Zhang & Huiyi Gao & Li Wan, 2022. "Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    5. Zhihua Hua & Haiyang Yu & Peng Jing & Caoyuan Song & Saifei Xie, 2023. "A Light-Weight Neural Network Using Multiscale Hybrid Attention for Building Change Detection," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    6. Dasen Li & Zhendong Yin & Yanlong Zhao & Wudi Zhao & Jiqing Li, 2023. "MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization," Agriculture, MDPI, vol. 13(6), pages 1-15, May.

    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:jsusta:v:14:y:2022:i:14:p:8915-:d:867705. 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.