Advertisement

A Review on Leaf Disease Detection Using Computer Vision Approach

  • Ranjita Rout
  • Priyadarsan ParidaEmail author
Conference paper
  • 82 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

Agriculture and its productivity has a good impact of the economic growth of every country. A path to good agricultural productivity depends on the disease susceptibility of the plants as well as early disease detection technologies for better production. Manual diagnosis of plant diseases needs expert knowledge along with awareness. So, automatic disease detection and identification of plants by application of computer vision approaches is of utmost importance. In this paper, different computer vision approaches for plant disease detection are analyzed. The results demonstrate the effectiveness of various methods in leaf disease detection.

Keywords

Computer vision Disease detection Thresholding Artificial Neural Network 

References

  1. 1.
    Camargo, A., Smith, J.S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 102, 9–21 (2009).  https://doi.org/10.1016/j.biosystemseng.2008.09.030CrossRefGoogle Scholar
  2. 2.
    Fang, Y., Ramasamy, R.P.: Current and prospective methods for plant disease detection, 537–561 (2015).  https://doi.org/10.3390/bios5030537
  3. 3.
    Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1–13 (2010).  https://doi.org/10.1016/j.compag.2010.02.007CrossRefGoogle Scholar
  4. 4.
    Mohanty, S.P., Hughes, D., Salathé, M.: Using deep learning for image-based plant disease detection (2016)Google Scholar
  5. 5.
    Lokesh, S., Naveenkumar, D., Rajesh, K., Kamath, G.A.R., Rathnam, M.J.: Leaf disease detection and grading using computer vision technology and fuzzy logic. Int. J. Innov. Res. Sci. Eng. Technol. 6, 279–287 (2017)Google Scholar
  6. 6.
    Radha, S.: Leaf disease detection using image processing. J. Chem. Pharm. Sci. 10, 670–672 (2017)Google Scholar
  7. 7.
    Golhani, K., Balasundram, S.K., Vadamalai, G., Pradhan, B.: A review of neural networks in plant disease detection using hyperspectral data. Inf. Process. Agric. 5, 354–371 (2018).  https://doi.org/10.1016/j.inpa.2018.05.002CrossRefGoogle Scholar
  8. 8.
    Devane, M.L., Weaver, L., Singh, S.K., Gilpin, B.J.: Fecal source tracking methods to elucidate critical sources of pathogens and contaminant microbial transport through New Zealand agricultural watersheds – a review. J. Environ. Manage. 222, 293–303 (2018).  https://doi.org/10.1016/j.jenvman.2018.05.033CrossRefGoogle Scholar
  9. 9.
    Du, X., Chen, B., Shen, T., Zhang, Y., Zhou, Z.: Effect of cropping system on radiation use efficiency in double-cropped wheat–cotton. Field Crops Res. 170, 21–31 (2015).  https://doi.org/10.1016/j.fcr.2014.09.013CrossRefGoogle Scholar
  10. 10.
    Kale, A.P., Sonavane, S.P.: IoT based smart farming: feature subset selection for optimized high-dimensional data using improved GA based approach for ELM. Comput. Electron. Agric. (2018).  https://doi.org/10.1016/j.compag.2018.04.027CrossRefGoogle Scholar
  11. 11.
    Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.-J.: Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017).  https://doi.org/10.1016/j.agsy.2017.01.023CrossRefGoogle Scholar
  12. 12.
    Kamlapurkar, S.R.: Detection of Plant Leaf Disease Using Image Processing Approach. Int. J. Sci. Res. Publ. 6, 73–76 (2016)Google Scholar
  13. 13.
    Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4, 41–49 (2017).  https://doi.org/10.1016/j.inpa.2016.10.005CrossRefGoogle Scholar
  14. 14.
    Bonnet, P., Joly, A., Boujemaa, N., Birnbaum, P., Mouysset, E., Barth, D.: The ImageCLEF 2011 plant images classification task. To cite this version: The ImageCLEF 2011 plant images classification task (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringGIET UniversityGunupur, RayagadaIndia

Personalised recommendations