Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture

TL;DR

This paper presents a machine learning approach using image processing and SVM to detect salmon fish diseases in aquaculture, achieving over 91% accuracy, aiding early diagnosis and disease control.

Contribution

The study introduces a novel image dataset and demonstrates effective disease classification using SVM with image augmentation, enhancing early detection in aquaculture.

Findings

SVM achieved 91.42% accuracy without augmentation.

SVM achieved 94.12% accuracy with augmentation.

Effective image preprocessing improves disease classification.

Abstract

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.