Precision farming in aquaculture: assessing gill health in Atlantic salmon (Salmo salar) using a non-invasive, AI-driven behavioural monitoring approach in commercial farms
Year: 2025
Type: article
Abstract: ) group behavioural profiles related to fish health and welfare. A machine vision algorithm applied to feed cameras on commercial farms was used to determine whether changes in gill health would induce visible group behavioural changes. Video cameras were deployed in all cages at two Scottish Atlantic salmon marine farms. One cage at each farm was also equipped with additional cameras (5 and 4 at sites A and B, respectively) to provide higher spatial coverage of fish behaviour and distribution. The algorithm processed video footage from these cameras and produced behavioural data termed 'activity' (%), which encompasses fish abundance, speed, and shoal cohesion. Additionally, gill health, Operational Welfare Indicators (OWI), mortality, and Specific Feeding Rate (SFR) were scored weekly at both sites. During summer 2023, gill health issues arose at both farms, leading to fish stress reflected in the behavioural data. For two months prior to the onset of poor gill health, the average (± standard deviation) activity levels of the fish across all cages were 25.6 ± 10.5% and 24.9 ± 7.0% for Farm A and B, respectively. After gill health was compromised, the activity rose significantly for two months in all cages with a mean of 43.6 ± 15.1% and 32.6 ± 9.6%, respectively. A generalised linear mixed model revealed that Proliferative Gill Disease (PGD) was the main driver of this increase in activity. This increase in activity coincided with fish migration to the centre of the cage, meaning tighter shoaling, which is a normal stress response often seen in relation to predators and other environmental or health stressors. The use of behaviour as a non-invasive welfare indicator and the potential to use artificial intelligence to automate the process of behavioural identification allows farmers to improve welfare conditions and ensure industry sustainability. Supplementary Information: The online version contains supplementary material available at 10.1186/s44365-025-00020-8.
Institution: University of Stirling
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Life below water
Open Access status: hybrid
Estimated APC paid: $1,990
Funder: Innovate UK
OpenAlex ID: W4413378539