Precision farming in aquaculture: non-invasive monitoring of Atlantic salmon (Salmo salar) behaviour in response to environmental conditions in commercial sea cages for health and welfare assessment - Burke, Meredith; Nikolic, Dragana; Fabry, Pieter; Rishi, Hemang; Telfer, Trevor; Rey Planellas, Sonia (Frontiers in Robotics and AI, 2025)
Studies show that Atlantic salmon in captivity adjust their distribution in sea cages based on environmental gradients like temperature, waves, and photoperiod. This study used a computer vision algorithm at three marine farms to analyse fish group swimming behaviour termed “activity” (measured in percent), which includes fish abundance, speed, and shoal cohesion. The activity metric inferred the depth distribution of the main fish group and was analysed with respect to environmental conditions to explore potential behavioural drivers and used to assess changes in fish behaviour in response to a stressor, a storm event. During winter conditions, Farms A and B showed distinct thermal stratification, with fish activity demonstrating preference for the warmer lower water column (39.6 ± 15.3% and 27.5 ± 10.2%) over the upper water column (16.3 ± 5.7% and 18 ± 3.3%; p < 0.001). At Farm C, with thermally homogenous water, fish activity was similarly distributed between the upper (18.2 ± 6.9%) and lower (17.7 ± 7.6%) water column. Severe weather increased wave heights, influencing fish horizontal distribution differently at Farms B and C. At Farm B, a deeper site, fish remained in the warmer lower water column and avoided surface waves, while at Farm C, with shallower cages, they moved toward the side of the cage nearest the centre of the farm, presumably less exposed due to nearby cages. Understanding fish behavioural responses to environmental conditions can inform management practices, while using cameras with associated algorithms offers a powerful, non-invasive tool for continuously monitoring and safeguarding fish health and welfare.