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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
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    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.
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    Cited by:

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    2. 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.
    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. 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.
    5. 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.
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    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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.

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