Plant Leaf Disease Detection Using Adaptive Neuro-Fuzzy Classification

  • Hiteshwari SabrolEmail author
  • Satish Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)


The paper deals with classification of different types of diseases of tomato and brinjal/eggplant. The patterns of the diseases are considered as a feature. It may be possible that the diseases are recognized by its texture patterns. A method that uses the texture patterns of the diseases in pure grayscale is applied for feature extraction purpose. A dedicated GLCM matrix is used to compute the features. The ANFIS based classification model is used for disease recognition by classification. The pattern based features with ANFIS recognition gives accuracy of 90.7% and 98.0% for TPDS 1.0 and BPDS 1.0 datasets respectively.


Plant disease recognition GLCM Adaptive neuo-fuzzy inference system 


  1. 1.
    Sanyal, P., Bhattacharya, U., Parui, S.K., Bandyopadhyay, S.K., Patel, S.: Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity. In: Proceeding of 10th International Conference on Information Technology (ICIT 2007), pp. 85–90. IEEE, Orissa (2007)Google Scholar
  2. 2.
    Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B., Kulkarni, P.: Diagnosis and classification of grape leaf diseases using neural networks. In: Proceeding of 4th International Conference (ICCCNT), pp. 1–5. IEEE, Tiruchengode (2013)Google Scholar
  3. 3.
    Asfarian, A., Herdiyeni, Y., Rauf, A., Mutaqin, K.M.: Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. In: Proceeding of International Conference on Computer, Control, Informatics and Its Applications, pp. 77–81. IEEE, Jakarta (2014)Google Scholar
  4. 4.
    Arivazhagan, I.S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int.: CIGR J. 15(1), 211–217 (2013)Google Scholar
  5. 5.
    Kurniawati, N.N., Abdullah, S.N.H.S., Abdullah, S.: Investigation on image processing techniques for diagnosing paddy diseases. In: Proceeding of 2009 International conference on Soft Computing and Pattern Recognition, pp. 272–277. IEEE, Malacca (2009)Google Scholar
  6. 6.
    Kurniawati, N.N., Abdullah, S.N.H.S., Abdullah, S.: Texture analysis for diagnosing paddy disease. In: Proceeding of 2009 International Conference on Electrical Engineering and Informatics, pp. 23–27. IEEE, Selangor (2009)Google Scholar
  7. 7.
    Kai, S., Zhikun, L., Hang, S., Chunhong, G.: A research of maize disease image recognition of corn based on BP networks. In: Third International Conference on Measuring Technology and Mechatronics Automation, Shangshai, pp. 246–249 (2011)Google Scholar
  8. 8.
    Pujari, J.D., Yakkundimath, R., Byadgi, A.S.: SVM and ANN based classification of plant diseases using feature reduction technique. Int. J. Interact. Multimed. Artif. Intell. 3(7), 3–14 (2016)Google Scholar
  9. 9.
    Pujari, J.D., Yakkundimath, R., Byadgi, A.S.: Identification and classification of fungal disease affected on agriculture/horticulture crops using image processing techniques. In: IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, pp. 1–4 (2014)Google Scholar
  10. 10.
    Bhange, M., Hingoliwala, H.A.: Smart farming: pomegranate disease detection using image processing. In: Proceedings of Second International Symposium on Computer Vision and Internet (VisionNet’ 2015), Procedia Computer Science, vol. 58, pp. 280–288 (2015)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Singh, V., Mishra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4, 41–49 (2017)Google Scholar
  13. 13.
  14. 14.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Englewood Cliffs (2006)Google Scholar
  15. 15.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  16. 16.
    Saki, F., Tahmasbi, A., Soltanian-Zadeh, H., Shokouhi, S.B.: Fast opposite weight learning rules with application in breast cancer diagnosis. Comput. Biol. Med. 43(1), 32–41 (2013)CrossRefGoogle Scholar
  17. 17.
    Jang, J.S.R.: ANFIS: adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  18. 18.
    Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, New York (1997)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationsDAV University JalandharPunjabIndia
  2. 2.Department of Computer Science and ApplicationsP.U. SSG RegionalHoshiarpurIndia

Personalised recommendations