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Swimming Performance Assessment in Fishes

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Computer Vision Detects an Association Between Gross Gill Score and Ventilation Rates in Farmed Atlantic Salmon

Quynh Le Khanh Vo^1,2, Kylie A Pitt¹, Colin Johnston³

¹School of Environment and Science, Australian Rivers Institute, Griffith University, Gold Coast, Queensland, Australia.

Journal of Fish Diseases

|August 26, 2025

Summary

Computer vision non-invasively monitors Atlantic salmon gill health by linking fish ventilation rates to gill scores. This method aids in identifying potential health issues for improved fish welfare in aquaculture.

Keywords:

AGD amoebic gill disease aquaculture artificial intelligence deep learning fish welfare

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Published on: February 26, 2016

Area of Science:

Aquaculture
Animal Health
Computer Vision

Background:

Poor gill health in farmed Atlantic salmon impacts welfare and causes respiratory distress.
Current gill health monitoring relies on manual 'gill scoring,' which is labor-intensive and stressful for fish.
Factors like amoebic gill disease (AGD), jellyfish stings, and toxic algae contribute to poor gill health.

Purpose of the Study:

To test a non-invasive computer vision approach for assessing Atlantic salmon gill health.
To investigate the association between gross gill score and fish ventilation rates in commercial salmon farms.
To determine if computer vision can assist in identifying health and welfare issues in farmed salmon.

Main Methods:

A computer vision model detected fish heads and classified mouth states (open/closed) using a convolutional neural network.
A tracking-by-detection method estimated ventilation rates by mouth movement frequency.
Ventilation rates were analyzed alongside gross gill scores and environmental data (temperature, dissolved oxygen, fish weight) from 240 videos.

Main Results:

Multiple linear regression revealed a positive association between ventilation rates and gross gill score.
The computer vision method successfully estimated fish ventilation rates.
While AGD was not confirmed, gill scores indicated general gill health status.

Conclusions:

The computer vision approach shows potential for non-invasive monitoring of salmon gill health.
This method can assist aquaculture management in identifying fish requiring further health assessments.
The technology offers a way to enhance fish welfare oversight and management practices in salmon farming.