Disease Detection on the Plant Leaves by Deep Learning

  • P. GoncharovEmail author
  • G. Ososkov
  • A. Nechaevskiy
  • A. Uzhinskiy
  • I. Nestsiarenia
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 799)


Plant disease detection by using different machine learning techniques is very popular field of study. Many promising results were already obtained but it is still only few real life applications that can make farmer’s life easier. The aim of our research is solving the problem of detection and preventing diseases of agricultural crops. We considered several models to identify the most appropriate deep learning architecture. As a source of the training data, we use the PlantVillage open database. During research, the problem with PlantVillage images collection was detected. The synthetic nature of the collection can seriously affect the accuracy of the neural model while processing real-life images. We collected a special database of the grape leaves consisting of four set of images. Deep siamese convolutional network was developed to solve the problem of the small image databases. Accuracy over 90% was reached in the detection of the Esca, Black rot and Chlorosis diseases on the grape leaves. Comparative results of various models and plants using are presented.


Machine learning Statistical models Siamese networks Plant disease detection Transfer learning 


  1. 1.
    Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7 (2016). Article: 1419Google Scholar
  2. 2.
    Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)CrossRefGoogle Scholar
  3. 3.
    Cortes E.: Plant Disease Classification Using Convolutional Networks and Generative Adverserial Networks. Stanford University Reports, Stanford (2017)Google Scholar
  4. 4.
    Fuentes, A., Yoon, S., Kim, S.C., Park, D.S.: A robust deep-learning-based detector for real-time tomato plant diseases and pest recognition. Sensors 17(9), 2022 (2017)CrossRefGoogle Scholar
  5. 5.
    Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.: Deep learning for image-based Cassava disease detection, frontiers in plant science. Front. Plant Sci. 8, 1852(2017)Google Scholar
  6. 6.
    Lua, J., Hua, J., Zhaoa, G., Meib, F., Zhanga, C.: An in-field automatic wheat disease diagnosis system. Comput. Electron. Agric. 142PA, 369–379 (2017)CrossRefGoogle Scholar
  7. 7.
    Ronnel, R., Atole, A.: Multiclass deep convolutional neural network classifier for detection of common rice plant anomalies. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 9(1) (2018)Google Scholar
  8. 8.
    He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  10. 10.
    Szegedy, C. et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  11. 11.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint (2016)Google Scholar
  12. 12.
    Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)
  13. 13.
    Kotikalapudi, R.: contributors (2017). keras-vis (2017)Google Scholar
  14. 14.
    Ososkov, G., Goncharov, P.: Shallow and deep learning for image classification. Opt. Mem. Neural Netw. 26(4), 221–248 (2017)CrossRefGoogle Scholar
  15. 15.
    Ososkov, G., Goncharov, P.: Two-stage approach to image classification by deep neural networks. In: EPJ Web of Conferences, vol. 173, p. 01009. EDP Sciences (2018)Google Scholar
  16. 16.
    Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)Google Scholar
  17. 17.
    Powers, D.: Evaluation: from precision recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetGoogle Scholar
  18. 18.
    Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • P. Goncharov
    • 1
    Email author
  • G. Ososkov
    • 2
  • A. Nechaevskiy
    • 2
  • A. Uzhinskiy
    • 2
  • I. Nestsiarenia
    • 1
  1. 1.Sukhoi State Technical University of GomelGomelBelarus
  2. 2.Joint Institute for Nuclear ResearchDubnaRussia

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