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Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Author

Listed:
  • Shengyi Zhao

    (Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Yun Peng

    (Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Jizhan Liu

    (Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Shuo Wu

    (Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

Abstract

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:7:p:651-:d:592317
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    References listed on IDEAS

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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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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