Detection of Avian Pox Disease using K-Means and Svm Classifer
Pooja U B1, Shreya R Joshi2, Apoorva.P3
1Pooja U B, Department of Computer Science Amrita School of Arts and Sciences, Mysuru. Amrita Vishwa Vidyapeetham, Karnataka, India.
2Shreya R Joshi, Department of Computer Science Amrita School of Arts and Sciences, Mysuru. Amrita Vishwa Vidyapeetham, Karnataka, India.
3Apoorva. P, Department of Computer Science Amrita School of Arts and Sciences, Mysuru. Amrita Vishwa Vidyapeetham, Karnataka, India.
Manuscript received on 04 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3238-3241 | Volume-8 Issue-9, July 2019 | Retrieval Number: I9002078919/19©BEIESP | DOI: 10.35940/ijitee.I9002.078919
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: This paper discusses about various methods involved in detection of avian pox in the birds using images. Digital images are corrupted while sending and receiving the images because of noisy sensors which degrade the quality of image. Pre-processing becomes an initial and crucial step in image processing to remove the noise and maintain fine details and texture of the image. Pre-processed images can be used for further work. Mean, Median, Weiner, Mean Maximum, Mean Minimum filters are used and performance tests are made using Signal Noise Ratio. Based on the performance test, removal of impulse noise is well done by Median filter and produces the best result when compared to other filters. K-Means clustering and SVM are used for identification of the disease.
Keywords: Avian Pox, Pre-Processing, Median Filter, K-Means, SVM
Scope of the Article: Classification