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SVM Based Plant Diseases Detection using Image Processing

Bharath Kumar R1 , Balakrishna K2 , hreyas M S3 , onu S4 , Anirudh H5 , Abhishek B J6

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1263-1266, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.12631266

Online published on May 31, 2019

Copyright © Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J, “SVM Based Plant Diseases Detection using Image Processing,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1263-1266, 2019.

MLA Style Citation: Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J "SVM Based Plant Diseases Detection using Image Processing." International Journal of Computer Sciences and Engineering 7.5 (2019): 1263-1266.

APA Style Citation: Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J, (2019). SVM Based Plant Diseases Detection using Image Processing. International Journal of Computer Sciences and Engineering, 7(5), 1263-1266.

BibTex Style Citation:
@article{R_2019,
author = {Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J},
title = {SVM Based Plant Diseases Detection using Image Processing},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1263-1266},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4399},
doi = {https://doi.org/10.26438/ijcse/v7i5.12631266}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.12631266}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4399
TI - SVM Based Plant Diseases Detection using Image Processing
T2 - International Journal of Computer Sciences and Engineering
AU - Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1263-1266
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

Plants are affected by a disease which leads to the variation in the growth stages of it’s and finally affects the throughput from it. Identification of the plant leaves diseases id the key role in preventing the losses in farming, where it’s a challenging to detect multi plant diseases. Here four major diseases affected by the plant are selected like Alternaria Alternata, Anthracnose, Bacterial Blight and Cercospora Leaf Spot and also addition with the healthy leaves using image processing technologies. The algorithm consist of a image pre-processing, image segmentation, feature extraction and finally with classification method.

Key-Words / Index Term

Image processing, Segmentation, Feature Extraction, Support Vector Machine, Gray-Level Cooccurrence Matrix (GLCM).

References

[1]. Naikwadi, NiketAmoda, “Advances in image processing for detection of plant diseases”, International journal of application or innovation in engineering and managrment(IJAIEM) Volume 2,Issue11,November 2013
[2]. Arti N. Rathod, Bhavesh A. Tanawala, Vatsal H. Shah, International Journal of Advance Engineer ing and Research Development (IJAERD) Volume 1,Issue 6,June 2014, e-ISSN: 2348 - 4470
[3]. Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz, Ali Yeon Bin Md Shakaff Rohani Binti S Mohamed Farook, “Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques”, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.
[4]. Mrunalini R. Badnakhe, Prashant R. Deshmukh, “Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue, March 2012.
[5]. Prakash and K Thangadurai., 2016. Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study. Indian Journal of Science and Technology, Vol. 9, pp. 1-6.