IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i8p707-d602326.html
   My bibliography  Save this article

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Author

Listed:
  • Jinzhu Lu

    (Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
    School of Mechanical Engineering, Xihua University, Chengdu 610039, China)

  • Lijuan Tan

    (Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
    School of Mechanical Engineering, Xihua University, Chengdu 610039, China)

  • Huanyu Jiang

    (College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China)

Abstract

Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

Suggested Citation

  • Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:8:p:707-:d:602326
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/8/707/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/8/707/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prakhar Bansal & Rahul Kumar & Somesh Kumar, 2021. "Disease Detection in Apple Leaves Using Deep Convolutional Neural Network," Agriculture, MDPI, vol. 11(7), pages 1-23, June.
    2. Feng Qin & Dongxia Liu & Bingda Sun & Liu Ruan & Zhanhong Ma & Haiguang Wang, 2016. "Identification of Alfalfa Leaf Diseases Using Image Recognition Technology," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-26, 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. 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.
    2. Sen Lin & Yucheng Xiu & Jianlei Kong & Chengcai Yang & Chunjiang Zhao, 2023. "An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture," Agriculture, MDPI, vol. 13(3), pages 1-20, February.
    3. Xia Hao & Man Zhang & Tianru Zhou & Xuchao Guo & Federico Tomasetto & Yuxin Tong & Minjuan Wang, 2021. "An Automatic Light Stress Grading Architecture Based on Feature Optimization and Convolutional Neural Network," Agriculture, MDPI, vol. 11(11), pages 1-17, November.
    4. 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.
    5. Mosleh Hmoud Al-Adhaileh & Amit Verma & Theyazn H. H. Aldhyani & Deepika Koundal, 2023. "Potato Blight Detection Using Fine-Tuned CNN Architecture," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    6. Jinzhu Lu & Kaiqian Peng & Qi Wang & Cong Sun, 2023. "Lettuce Plant Trace-Element-Deficiency Symptom Identification via Machine Vision Methods," Agriculture, MDPI, vol. 13(8), pages 1-27, August.
    7. Hamed Alghamdi & Turki Turki, 2023. "PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features," Agriculture, MDPI, vol. 13(5), pages 1-19, May.
    8. Fahman Saeed & Muhammad Hussain & Hatim A. Aboalsamh, 2022. "Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    9. Li Zhang & Qun Hao & Jie Cao, 2023. "Attention-Based Fine-Grained Lightweight Architecture for Fuji Apple Maturity Classification in an Open-World Orchard Environment," Agriculture, MDPI, vol. 13(2), pages 1-20, January.
    10. Hieu T. T. L. Pham & Mahdi Rafieizonooz & SangUk Han & Dong-Eun Lee, 2021. "Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    11. 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.

    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. Mariana Rockenbach de à vila & Raquel Esteban & Miguel Dall Agnol & José F Morán, 2020. "Physiological traits involved in grazing tolerance of alfalfa genotypes," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 25(2), pages 102-106, November.
    3. Sen Lin & Yucheng Xiu & Jianlei Kong & Chengcai Yang & Chunjiang Zhao, 2023. "An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture," Agriculture, MDPI, vol. 13(3), pages 1-20, February.
    4. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
    5. Hamna Waheed & Noureen Zafar & Waseem Akram & Awais Manzoor & Abdullah Gani & Saif ul Islam, 2022. "Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    6. Yiwei Zhong & Baojin Huang & Chaowei Tang, 2022. "Classification of Cassava Leaf Disease Based on a Non-Balanced Dataset Using Transformer-Embedded ResNet," Agriculture, MDPI, vol. 12(9), pages 1-18, September.
    7. Xulu Gong & Shujuan Zhang, 2023. "A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN," Agriculture, MDPI, vol. 13(2), pages 1-15, January.
    8. Ranbing Yang & Yuming Zhai & Jian Zhang & Huan Zhang & Guangbo Tian & Jian Zhang & Peichen Huang & Lin Li, 2022. "Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation," Agriculture, MDPI, vol. 12(9), pages 1-17, September.

    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:11:y:2021:i:8:p:707-:d:602326. 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.