Identification of Plant Leaf Diseases Based on Inception V3 Transfer Learning and Fine-Tuning

  • Zhenping Qiang
  • Libo He
  • Fei DaiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


Crop disease is a major factor currently to jeopardize agricultural production activities. In recent years, with the great success of deep learning technology in the field of image classification and image recognition, and with the convenient acquisition of crop leaf images, it is possible to automatically identify crop disease through deep learning based on plant leaf disease images. This paper mainly completed the research and analysis of leaf disease identification of agricultural plants based on Inception-V3 neural network model transfer learning and fine-tuning. A large number of model accuracy tests are carried out by training neural networks with different parameters. When the network parameter Batch is set to 100 and the learning rate is set to 0.01, the training precision and test precision of the network reach the maximum. Its training precision rate for crop disease image recognition in the PlantVillage DataSet is 95.8%, and the precision rate on the test set is as high as 93%, and far exceeding the accuracy of manual recognition. This fully proves that the deep learning model based on Inception-V3 neural network can effectively distinguish crop disease.


Crop disease identification Inception V3 Transfer learning Fine-tuning Deep learning 



This work is supported by the project of National Natural Science Foundation of China (11603016), Key Scientific Research Foundation Project of Southwest Forestry University (111827), Kunming Forestry Information Engineering Technology Research Center Fund Project (2015FBI06), and the project of Scientific Research Foundation of Yunnan Police Officer College (19A010).


  1. 1.
    Savary, S., Ficke, A., Aubertot, J.N., et al.: Crop losses due to diseases and their implications for global food production losses and food security. Food Secur. 4(4), 519–537 (2012)CrossRefGoogle Scholar
  2. 2.
    Barbedo, J.G.A.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 144, 52–60 (2016)CrossRefGoogle Scholar
  3. 3.
    Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)CrossRefGoogle Scholar
  4. 4.
    Kaur, S., Pandey, S., Goel, S.: Plants disease identification and classification through leaf images: a survey. Archiv. Comput. Methods Eng. 26(2), 507–530 (2019)CrossRefGoogle Scholar
  5. 5.
    Martinelli, F., Scalenghe, R., Davino, S., et al.: Advanced methods of plant disease detection. A review. Agron. Sustain. Dev. 35(1), 1–25 (2015)CrossRefGoogle Scholar
  6. 6.
    Jadhav, S.B., Patil, S.B.: Grading of soybean leaf disease based on segmented image using k-means clustering. Int. J. Adv. Res. Electr. Commun. Eng. 4(6), 1816–1822 (2015)Google Scholar
  7. 7.
    Rangel, B.M., Fernández, M.A., Murillo, J.C., et al.: KNN-based image segmentation for grapevine potassium deficiency diagnosis. In: IEEE International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 48–53 (2016)Google Scholar
  8. 8.
    Zhang, X., Li, D., Yang, W., et al.: A fast segmentation method for high-resolution color images of foreign fibers in cotton. Comput. Electron. Agric. 78(1), 71–79 (2011)CrossRefGoogle Scholar
  9. 9.
    Oberti, R., Marchi, M., Tirelli, P., et al.: Automatic detection of powdery mildew on grapevine leaves by image analysis: optimal view-angle range to increase the sensitivity. Comput. Electron. Agric. 104, 1–8 (2014)CrossRefGoogle Scholar
  10. 10.
    Phadikar, S., Sil, J., Das, A.K.: Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric. 90(90), 76–85 (2013)CrossRefGoogle Scholar
  11. 11.
    Gharge, S., Singh, P.: Image processing for soybean disease classification and severity estimation. In: Shetty, N., Prasad, N., Nalini, N. (eds.) Emerging Research in Computing, Information, Communication and Applications, pp. 493–500. Springer, New Delhi (2016). Scholar
  12. 12.
    Asfarian, A., Herdiyeni, Y., Rauf, A., et al.: Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. In: The International Conference on Computer, Control, Informatics and Its Applications, IC3INA 2013. IEEE (2013)Google Scholar
  13. 13.
    Wang, L., Dong, F., Guo, Q., et al.: Improved rotational kernel transformation directional feature for recognition of wheat stripe rust and powdery mildew. In: IEEE 7th International Conference on Image and Signal Processing CISP, Dalian, pp. 286–291 (2014)Google Scholar
  14. 14.
    Sanyal, P., Patel, S.C.: Pattern recognition method to detect two diseases in rice plants. J. Photogr. Sci. 56(6), 319–325 (2013)Google Scholar
  15. 15.
    Pires, R.D.L., Goncalves, D.N., Oruê, J.P.M., et al.: Local descriptors for soybean disease recognition. Comput. Electron. Agric. 125, 48–55 (2016)CrossRefGoogle Scholar
  16. 16.
    Al-Saffar, A.A.M., Tao, H., Talab, M.A.: Review of deep convolution neural network in image classification. In: 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET). IEEE (2018)Google Scholar
  17. 17.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
  18. 18.
    Ullah, K.R., Xiaosong, Z., Rajesh, K.: Analysis of ResNet and GoogleNet models for malware detection. J. Comput. Virol. Hacking Tech. 15, 29–37 (2018)Google Scholar
  19. 19.
    Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)Google Scholar
  20. 20.
    Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261v1 (2016)
  21. 21.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)Google Scholar
  22. 22.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  23. 23.
    Targ, S., Almeida, D., Lyman, K.: ResNet in ResNet: generalizing residual architectures. arXiv preprint arXiv:1603.08029 (2016)
  24. 24.
    Jie, H., Li, S., Albanie, S., et al.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)
  25. 25.
    Lecun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  26. 26.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. 25(2), 1097–1105 (2012)Google Scholar
  27. 27.
    PlantVillage. Accessed 20 Sept 2019

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Big Data and Intelligent EngineeringSouthwest Forestry UniversityKunmingChina
  2. 2.Information Security CollegeYunnan Police CollegeKunmingChina

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