Fruit Disease Detection Using GLCM And SVM Classifier

Authors(4) :-Anu S, Nisha T, Ramya R, Rizuvana Farvin M

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 365-371
Manuscript Number : CSEIT195221
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Anu S, Nisha T, Ramya R, Rizuvana Farvin M, "Fruit Disease Detection Using GLCM And SVM Classifier", International 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 :
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