  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 disease. 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 market. 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 examine 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.
