Deep Learning Approaches for Tomato Plant Disease Detection


Md Abdur Rahman,Associate Professor of Computer Science, Department of Mathematics, Jahangirnagar University, Savar, Dhaka, Bangladesh


Agriculture is the mainstream to keep pace in the Bangladeshi economy. Plant disease became a threat to food security as it is a very important factor to deteriorate the quality and quantity of harvest. Therefore, it is important to detect the plant diseases early which results in interrupting from falling the massive destruction of harvest. But, an erroneous diagnosis of the disease results in the inappropriate use of pesticides. In order to enhance the production quality and quantity, a deep learning-based approach is proposed to detect the tomato leaf diseases, and then classify the types of the disease using image dataset. This proposed approach trained two model architectures: inception V3 and Convolutional Neural Network (CNN). Inception V3 performs well and reaches a success rate with a 96.11% in order to identify whether the specific plant leaf is infected or healthy. The success rate is significant and makes this approach as a very useful way or early forewarning tool, and this approach might be an essential system to operate real agriculture fields. As the detection accuracy is recorded as 94.72% for CNN. We confirm that it achieves the experimental results with 94.72% and 96.11% for the detection and classification of infected leaves from dataset for CNN and Inception V3 respectively.



Deep learning, Convolutional Neural Network (CNN), Inception model, Transfer learning, PoolinglLayer, Softmax layer


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  • APA:
    Rahman,M.A.(2020). Deep Learning Approaches for Tomato Plant Disease Detection. International Journal of Hybrid Information Technology, 13(2), 71-78. 10.21742/IJHIT.2020.13.2.06
  • Harvard:
    Rahman,M.A.(2020). "Deep Learning Approaches for Tomato Plant Disease Detection". International Journal of Hybrid Information Technology, 13(2), pp.71-78. doi:10.21742/IJHIT.2020.13.2.06
  • IEEE:
    [1] M.A.Rahman, "Deep Learning Approaches for Tomato Plant Disease Detection". International Journal of Hybrid Information Technology, vol.13, no.2, pp.71-78, Sep. 2020
  • MLA:
    Rahman Md Abdur. "Deep Learning Approaches for Tomato Plant Disease Detection". International Journal of Hybrid Information Technology, vol.13, no.2, Sep. 2020, pp.71-78, doi:10.21742/IJHIT.2020.13.2.06


Creative Commons License
© 2020 Md Abdur Rahman. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


  • Volume 13, No. 2, 2020
  • ISSN(p):1738-9968
  • ISSN(e):2652-2233
  • Published:Sep. 2020