Atlantic salmon activity data derived from AI algorithm at commercial aquaculture farm

Abstract

As the aquaculture industry is growing, more sophisticated technology is required to monitor farms and ensure sustainability and good fish welfare, in line with the precision livestock farming concept. Using behaviour as a non-invasive form of monitoring, in combination with artificial intelligence and machine learning, can allow for higher control over farm management. The goal of this study was to use a novel machine learning algorithm to quantify and assess changes to farmed Atlantic salmon (Salmo salar) behaviour related to fish health and welfare status. Main behaviours recorded were shoal-like cohesion, feeding, swimming activity and fish distribution in the cage. Video cameras were deployed within all cages in two Scottish Atlantic salmon marine farms. Furthermore, one cage in each farm was equipped with additional cameras (5 and 4 for site 1 and 2, respectively), for higher spatial coverage of fish behaviour and distribution throughout the cage. The algorithm processed video footage from these cameras and outputted behavioural data termed ‘activity’, which encompasses fish abundance, speed, and shoal cohesion. This dataset includes this activity data along with the date/time, depth of the camera, and temperature at the camera's location.