Fruit Disease Detection Using GLCM And SVM Classifier

Authors

  • Anu S  Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India
  • Nisha T  Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India
  • Ramya R  Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India
  • Rizuvana Farvin M  Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195221

Keywords:

Analytics, HSV, Gray level co-occurrence matrix, SVM.

Abstract

Analytics plays a critical role in detecting and analyzing the diseases. The proposed system identifies the fruits that are affected with diseases. It is done by collecting the raw data which is subjected to pre-processing. It results in a HSV (hue, saturation, value) converted image. After pre-processing, the resized format of the data is used to extract the information. In feature extraction the image is segmented and converted into matrix using Gray level co-occurrence matrix algorithm. The further classification is done and result is represented in the form of a decision tree using Support Vector Machine (SVM). The disease that affected the fruit is displayed along with the right fertilizer to be used for the plant.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Anu S, Nisha T, Ramya R, Rizuvana Farvin M, " Fruit Disease Detection Using GLCM And SVM Classifier, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.365-371, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195221