Advertisement

Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier

  • Jaskaran Singh
  • Harpreet Kaur
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

The plant disease detection is the technique which can detect disease from the plant leaves. The plant disease detection has various steps which are textural feature analysis, segmentation, and classification. This research paper is based on the plant disease detection using the KNN classifier with GLCM algorithm. In the proposed method, the image is taken as input which is preprocessed, GLCM algorithm is applied for the textural feature analysis, k-means clustering is applied for the region-based segmentation, and KNN classifier is applied for the disease prediction. The proposed technique is implemented in MATLAB and simulation results show up to 97% accuracy.

Keywords

SVM KNN GLCM k-means Region-based segmentation 

References

  1. 1.
    Rastogi A, Arora R, Sharma S, Leaf disease detection and grading using computer vision technology and fuzzy logic. In: 2015 2nd international conference on signal processing and integrated networks (SPIN)Google Scholar
  2. 2.
    Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: 2015 international conference on computing communication control and automationGoogle Scholar
  3. 3.
    Waghmare H, Kokare R, Dandawate Y (2016) Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In: 2016 3rd international conference on signal processing and integrated networks (SPIN)Google Scholar
  4. 4.
    Prakash RM, Saraswathy GP, Ramalakshmi G (2017) Detection of leaf diseases and classification using digital image processing. In: 2017 international conference on innovations in information, embedded and communication systems (ICIIECS)Google Scholar
  5. 5.
    Kaur K, Marwaha C (2017) Analysis of diseases in fruits using image processing techniques. In: International conference on trends in electronics and informatics ICEI 2017Google Scholar
  6. 6.
    Dhaware CG, Wanjale KH (2017) A modern approach for plant leaf disease classification which depends on leaf image processing. In: 2017 international conference on computer communication and informatics (ICCCI—2017)Google Scholar
  7. 7.
    Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 Conference on advances in signal processing (CASP)Google Scholar
  8. 8.
    Rajan P, Radhakrishnan B, Suresh LP (2016) Detection and classification of pests from crop images using support vector machine. In: 2016 international conference on emerging technological trends (ICETT)Google Scholar
  9. 9.
    Duan X, Zhao T, Li T, Liu J, Zou L, Zhang L (2017) Method for diagnosis of on-load tap changer based on wavelet theory and support vector machine. In: 2017. In: The 6th international conference on renewable power generation (RPG)Google Scholar
  10. 10.
    Parvez A, Phadke AC (2017) Efficient implementation of GLCM based texture feature computation using CUDA platform. In: International conference on trends in electronics and informatics, ICEIGoogle Scholar
  11. 11.
    Zhang Zhongheng (2016) Introduction to machine learning: k-nearest neighbors. Ann Transl Med 4(11):218CrossRefGoogle Scholar
  12. 12.
    Islam M, Dinh A, Wahid K (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ECE DepartmentChandigarh UniversityGharuanIndia
  2. 2.CSE DepartmentChandigarh UniversityGharuanIndia

Personalised recommendations