Production of fish in land-based aquaculture contributes significantly to the production of animal protein in the human food-supply chain in the Nordic countries. Recirculation aquaculture systems (RAS) provide an environmentally sustainable solution that is becoming more and more relevant for current production systems. However, considerable challenges with these production systems means narrow production margins and uncertainties regarding fish health and production performance. An extensive amount of information on water quality, feed use and health parameters are gathered through monitoring of the fish and their environment via sensors. In most cases, these data are stored in separate datasystems, not utilizing the huge potential for more precise monitoring and reporting of the realtime health, welfare and growth of the fish. We aim to integrate such data from many farms and production cycles and through the usage of Artificial Intelligence (AI), enabling the farmers to move from experience-based to knowledge-based decision making. This will enable a more sustainable production with regards to production margins, environmental impact and fish health and -welfare. The best indicator of health and performance is the behaviour of the fish themselves. One major challenge in RAS is waste of feed. We will use AI and statistical models to develop a closed loop control of feeding to reduce feed waste and optimize growth, moving from a manual analysis and response to a knowledge-based automatic one. This will be done by the application of deep learning tools on video sequences of fish, teaching computer systems how the fish react when they are hungry and when they have been feed. In addition, statistical models will be used on the integrated datasources and video sequences to develop a system for early detection and warning of upcoming disease events, thus giving the farmer time to react in the narrow time-window between an adverse health event and death.
Project leader: Britt Bang Jensen