An Effective Fuzzy Controlled Filter for Feature Extraction Method

  • Mohamad AlshahadatEmail author
  • Bülent Bilgehan
  • Cemal Kavalcıoğlu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


Atomization of agricultural tasks such as disease removal is increasingly growing in European countries and thus accurate techniques are significantly required for efficient use of chemicals e.g. pesticides. In the present study, a computer vision-based technique is proposed which can be used for site specific spread of anti-fungal chemicals on strawberry leaves which alleviates yield’s quality and quantity. The proposed technique mainly constitutes a band-pass filter for fungi-infection localization. The merit of this research work is taking into account human perception of fungi visual aspects to lower the computational load and ease the deploying technique on single chip processor for real-time application.


Computer vision Cypriot/mediterranean strawberry Fungi-infection Band-pass filter Filter coefficients 


  1. Al-Bashish, D., Braik, M., Bani-Ahmad, S.: Detection and classification of leaf diseases using K-means-based segmentation and neural networks-based classification. Inf. Technol. J. 2(3), 267–275 (2011)Google Scholar
  2. Boissard, P., Martin, V., Moisan, S.: A cognitive vision approach to early pest detection in greenhouse crops. Comput. Electron. Agric. 60, 81–93 (2008)CrossRefGoogle Scholar
  3. Durmus, H., Gunes, E.O., Kirci, M., Ustundag, B.B.: The design of general purpose autonomous agricultural mobile-robot. In: 4th International Conference on Agro-Geoinformatics, pp. 49–53. IEEE, Istanbul (2015)Google Scholar
  4. El-Helly, M., Rafea, A., El-Gammal, S.: An integrated image processing system for leaf disease detection and diagnosis. In: IICAI 2003, pp. 1182–1195 (2003)Google Scholar
  5. Pujari, J., Yakkundimath, R., Byadgi, A.: 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, pp. 31–34. IEEE, Coimbatore (2014)Google Scholar
  6. Kim, D.G., Burks, T.F., Qin, J., Bulanon, D.M.: Classification of grapefruit peel diseases using color texture feature analysis. Int. J. Agric. Biol. Eng. 35, 41–50 (2009)Google Scholar
  7. Rishi, N., Gill, J.S.: An overview on detection and classification of plant diseases in image processing. Int. J. Sci. Eng. Res. 3(5), 3–6 (2015)Google Scholar
  8. Özyapıcı, A.: On multiplicative and Volterra minimization methods. Numer. Algorithms 67(3), 623–636 (2014)MathSciNetCrossRefGoogle Scholar
  9. Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B., Yallur, P.S.: Leaf disease grading by machine vision and fuzzy logic. Int. J. Comput. Technol. Appl. 2(5), 1709–1716 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohamad Alshahadat
    • 1
    Email author
  • Bülent Bilgehan
    • 1
  • Cemal Kavalcıoğlu
    • 1
  1. 1.Department of Electrical and Electronic Engineering, Faculty of EngineeringNear East UniversityNicosia, TRNCTurkey

Personalised recommendations