Abstract

The proposed system is designed for automatic detection and classification of fish diseases in freshwater es-pecially Rangamati Kaptai Lake and Sunamganj Hoar area of Bangladesh. Our experimental result is indicating that the proposed approach is significantly an accurate and automatic detection and recognition of fish diseases. This study presents fish disease detection based on the K-means and C-means fuzzy logic clustering method to segment the filtering image. Gabor’s Filters and Gray Level Co-occurrence Matrix (GLCM) are used to extracts the features from the segmented regions. Finally Multi-Support Vector Machine (M-SVMs) is used for classification of the test image. The proposed system demonstrated a comparison between K-means clustering and C-means fuzzy logic. The proposed methodology gave 96.48% accuracy using K-means and 97.90% using C-means fuzzy logic which is the highest accuracy rate to compare other existing methods. The proposed system has been experimented in the MATLAB environment on infected fish images of Rangamati Kaptai Lake and Sumangan Hoar area. It is a challenging task of fisheries farming in Hoar areas and Lake areas to detect fish diseases initially. The proposed methodology can detect and classify different fish diseases in early stages and also contributes to improved results for fish disease detection.

Details

Title
Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh
Author
Sikder, Juel; Kamrul Islam Sarek; Das, Utpol Kanti
Publication year
2021
Publication date
2021
Publisher
Science and Information (SAI) Organization Limited
ISSN
2158107X
e-ISSN
21565570
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2655117685
Copyright
© 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.