Advertisement

Deep Learning Based Approach for Classification and Detection of Papaya Leaf Diseases

  • Rathan Kumar VeeraballiEmail author
  • Muni Sankar Nagugari
  • Chandra Sekhara Rao Annavarapu
  • Eswar Varma Gownipuram
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

In recent years, around the globe the horticulture crop outcomes falling down due to the devastating diseases, this impact will shows on yield of farmers such as quality and quantity of horticulture products, even in developed countries. Therefore, for prevention early observation and discovery of these diseases are very significant. In this paper we built a straight forward Convolution neural network on image classification for plant diseases, specifically for papaya plants, papaya suffering from Leaf Curl of Papaya, papaya mosaic. In a row, we propose for identification and classifying papaya leaves diseases a deep learning-based approach by using ResNet50 architecture as a convolutional neural network to stratify image data sets. Across globe in many disciplines deep learning has been employed. I.e. object tracking, text detection, image classification, action recognition. In deep learning different type of models, among Convolutional neural networks and Deep Belief Networks are frequently used models Convolutional neural networks has been exhibited extreme capabilities on image classification. The proposed model generated results are shown very usefulness of it, even under difficult conditions such as image size, pose, different resolution, illumination, complex back ground and alignment of actual images.

Keywords

Papaya mosaic ResNet50 Leaf Curl of Papaya 

References

  1. 1.
    Chollet, F.: Keras. https://github.com/fchollet/keras. Accessed 10 Nov 2016
  2. 2.
    Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., ALRahamneh, Z.: Fast and accurate detection and classification of plant diseases. Mach. Learn. 17(1) March 2011CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Barbedo, J.G.A., Koenigkan, L.V., Santos, T.T.: Identifying multiple plant diseases using digital image processing. Biosyst. Eng. 147, 104–116 (2016)CrossRefGoogle Scholar
  5. 5.
    Byun, H.S., Kil, E.J., Seo, H., Suh, S.S., Lee, T.K., Lee, J.H., Kim, J.K., Lee, K.Y., Ko, S.J., Lee, G.S., et al.: First report of papaya leaf curl virus in papayas in korea and recovery of its symptoms. Plant Dis. 100(9), 1958–1958 (2016)CrossRefGoogle Scholar
  6. 6.
    Cui, D., Zhang, Q., Li, M., Hartman, G.L., Zhao, Y.: Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosyst. Eng. 107(3), 186–193 (2010)CrossRefGoogle Scholar
  7. 7.
    Deng, L.: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inform. Process. 3(e2), 1–29 (2014).  https://doi.org/10.1017/ATSIP.2013.99CrossRefGoogle Scholar
  8. 8.
    Fuentes, A., Yoon, S., Kim, S.C., Park, D.S.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)CrossRefGoogle Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  10. 10.
    Hughes, D.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015)
  11. 11.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 689–696 (2011)Google Scholar
  12. 12.
    Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M., Borghese, A.N.: 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
  13. 13.
    Obile, W.: Ericsson mobility report, November 2016Google Scholar
  14. 14.
    Oerke, E.C.: Crop losses to pests. J. Agric. Sci. 144(1), 31–43 (2006)CrossRefGoogle Scholar
  15. 15.
    Patil, S., Chandavale, A.: A survey on methods of plant disease detection. Int. J. Sci. Res. (IJSR) 4, 1392–1396 (2015)CrossRefGoogle Scholar
  16. 16.
    Pawara, P., Okafor, E., Surinta, O., Schomaker, L., Wiering, M.: Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. In: ICPRAM, pp. 479–486 (2017)Google Scholar
  17. 17.
    Sannakki, S.S., Rajpurohit, V.S., Nargund, V., Kulkarni, P.: Diagnosis and classification of grape leaf diseases using neural networks. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). pp. 1–5. IEEE (2013)Google Scholar
  18. 18.
    Schor, N., Bechar, A., Ignat, T., Dombrovsky, A., Elad, Y., Berman, S.: Robotic disease detection in greenhouses: combined detection of powdery mildew and tomato spotted wilt virus. IEEE Robot. Autom. Lett. 1(1), 354–360 (2016)CrossRefGoogle Scholar
  19. 19.
    Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813 (2014)Google Scholar
  20. 20.
    Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 1–11 (2016).  https://doi.org/10.1155/2016/3289801CrossRefGoogle Scholar
  21. 21.
    Varun, P., Ranade, S., Saxena, S.: A molecular insight into papaya leaf curl–a severe viral disease. Protoplasma 254(6), 2055–2070 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rathan Kumar Veeraballi
    • 1
    Email author
  • Muni Sankar Nagugari
    • 1
  • Chandra Sekhara Rao Annavarapu
    • 2
  • Eswar Varma Gownipuram
    • 1
  1. 1.Sri Venkatesa Perumal College of Engineering and TechnologyPutturIndia
  2. 2.Indian Institute of Technology (Indian School of Mines)DhanbadIndia

Personalised recommendations