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A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification

Author

Listed:
  • Anil Bhujel

    (Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea
    Ministry of Communication and Information Technology, Kathmandu 44600, Nepal)

  • Na-Eun Kim

    (Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea)

  • Elanchezhian Arulmozhi

    (Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea)

  • Jayanta Kumar Basak

    (Smart Farm Research Center, Gyeongsang National University, Jinju 52828, Korea)

  • Hyeon-Tae Kim

    (Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea)

Abstract

Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:228-:d:742199
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    Citations

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    Cited by:

    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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