IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i10p1727-d947315.html
   My bibliography  Save this article

Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module

Author

Listed:
  • Xiang Zhang

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei 230031, China
    Science Island Branch, Graduate School of USTC, Hefei 230026, China)

  • Huiyi Gao

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei 230031, China
    Lu’an Branch, Anhui Institute of Innovation for Industrial Technology, Lu’an 237100, China)

  • Li Wan

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei 230031, China
    Lu’an Branch, Anhui Institute of Innovation for Industrial Technology, Lu’an 237100, China)

Abstract

Crop disease seriously affects food security and causes huge economic losses. In recent years, the technology of computer vision based on convolutional neural networks (CNNs) has been widely used to classify crop disease. However, the classification of fine-grained crop disease is still a challenging task due to the difficult identification of representative disease characteristics. We consider that the key to fine-grained crop disease identification lies in expanding the effective receptive field of the network and filtering key features. In this paper, a novel module (DC-DPCA) for fine-grained crop disease classification was proposed. DC-DPCA consists of two main components: (1) dilated convolution block, and (2) dual-pooling channel attention module. Specifically, the dilated convolution block is designed to expand the effective receptive field of the network, allowing the network to acquire information from a larger range of images, and to provide effective information input to the dual-pooling channel attention module. The dual-pooling channel attention module can filter out discriminative features more effectively by combining two pooling operations and constructing correlations between global and local information. The experimental results show that compared with the original networks (85.38%, 83.22%, 83.85%, 84.60%), ResNet50, VGG16, MobileNetV2, and InceptionV3 embedded with the DC-DPCA module obtained higher accuracy (87.14%, 86.26%, 86.24%, and 86.77%). We also provide three visualization methods to fully validate the rationality and effectiveness of the proposed method in this paper. These findings are crucial by effectively improving classification ability of fine-grained crop disease by CNNs. Moreover, the DC-DPCA module can be easily embedded into a variety of network structures with minimal time cost and memory cost, which contributes to the realization of smart agriculture.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1727-:d:947315
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/10/1727/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/10/1727/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jianwu Lin & Xiaoyulong Chen & Renyong Pan & Tengbao Cao & Jitong Cai & Yang Chen & Xishun Peng & Tomislav Cernava & Xin Zhang, 2022. "GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases," Agriculture, MDPI, vol. 12(6), pages 1-17, June.
    3. Shengyi Zhao & Yun Peng & Jizhan Liu & Shuo Wu, 2021. "Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
    4. 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)

    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. Yang Chen & Xiaoyulong Chen & Jianwu Lin & Renyong Pan & Tengbao Cao & Jitong Cai & Dianzhi Yu & Tomislav Cernava & Xin Zhang, 2022. "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification," Agriculture, MDPI, vol. 12(12), pages 1-22, November.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. Jianwu Lin & Xiaoyulong Chen & Renyong Pan & Tengbao Cao & Jitong Cai & Yang Chen & Xishun Peng & Tomislav Cernava & Xin Zhang, 2022. "GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases," Agriculture, MDPI, vol. 12(6), pages 1-17, June.
    12. Yuan-Kai Tu & Chin-En Kuo & Shih-Lun Fang & Han-Wei Chen & Ming-Kun Chi & Min-Hwi Yao & Bo-Jein Kuo, 2022. "A 1D-SP-Net to Determine Early Drought Stress Status of Tomato ( Solanum lycopersicum ) with Imbalanced Vis/NIR Spectroscopy Data," Agriculture, MDPI, vol. 12(2), pages 1-17, February.
    13. 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.
    14. 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.
    15. J. Dhakshayani & B. Surendiran, 2023. "M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers," Agriculture, MDPI, vol. 13(6), pages 1-19, June.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Bharathwaaj Sundararaman & Siddhant Jagdev & Narendra Khatri, 2023. "Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato ( Solanum lycopersicum ) Disease Management for Global Food Security: A Comprehensive Review," Sustainability, MDPI, vol. 15(15), pages 1-23, July.

    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:1727-:d:947315. 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.