Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture

被引:66
作者
Ahmed, Md Shoaib [1 ]
Aurpa, Tanjim Taharat [1 ]
Azad, Md. Abul Kalam [1 ]
机构
[1] Jahangirnagar Univ, Dhaka, Bangladesh
关键词
Fish Diseases; Aquaculture; Image Processing; Machine Learning; Support Vector Machine; Salmon Fish; SUPPORT VECTOR MACHINE;
D O I
10.1016/j.jksuci.2021.05.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fish diseases in aquaculture constitute a significant hazard to nutriment security. Identification of infected fishes in aquaculture remains challenging to find out at the early stage due to the dearth of necessary infrastructure. The identification of infected fish timely is an obligatory step to thwart from spreading dis-ease. In this work, we want to find out the salmon fish disease in aquaculture, as salmon aquaculture is the fastest-growing food production system globally, accounting for 70 percent (2.5 million tons) of the mar-ket. In the alliance of flawless image processing and machine learning mechanism, we identify the infected fishes caused by the various pathogen. This work divides into two portions. In the rudimentary portion, image pre-processing and segmentation have been applied to reduce noise and exaggerate the image, respectively. In the second portion, we extract the involved features to classify the diseases with the help of the Support Vector Machine (SVM) algorithm of machine learning with a kernel function. The processed images of the first portion have passed through this (SVM) model. Then we harmonize a comprehensive experiment with the proposed combination of techniques on the salmon fish image dataset used to exam-ine the fish disease. We have conveyed this work on a novel dataset compromising with and without image augmentation. The results have bought a judgment of our applied SVM performs notably with 91.42 and 94.12 percent of accuracy, respectively, with and without augmentation.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:5170 / 5182
页数:13
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