Bacterial and Virus affected Citrus Leaf Disease Classification using Smartphone and SVM
Utpal Barman1, Ridip Dev Choudhury2
1Utpal Barman, Research Scholar in the Department of Information Technology, Gauhati University.
2Dr. Ridip Dev Choudhury, Assistant Professor in the Department of Computer Science, IDOL, Gauhati University.
Manuscript received on 16 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 July 2019 | PP: 4220-4226 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3615078219/19©BEIESP | DOI: 10.35940/ijrte.B3615.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automatic detection of citrus leaves disease is very much essential for the better productivity of citrus. Citrus leaves are affected by bacteria, fungus and virus respectively. Farmer detects the diseases of the plant using laboratory, naked eyes or using expert’s view. The rural farmers often face difficulties to detect these diseases due to the non availability of the laboratories in their area. Here in this paper, a computer automation system is proposed to detect the diseases of citrus leaves on an early stage. Citrus leaves images are captured using Smartphone. Captured images are used to extract the different features of the citrus leaves samples using Gray Level Co-occurrence Matrix. Finally, citrus greening and citrus CTV images are classified from citrus healthy images using Gaussian kernel based support vector machine. Accuracy of the kernel is evaluated for the different values of Gamma parameter of kernel. The Gaussian kernel gives maximum accuracy (95.5%) with Gamma value 1.
Index Terms: Citrus, Disease, SVM, Kernel, Image Processing.
Scope of the Article: Image Processing and Pattern Recognition