As fish farming sites move to areas more exposed to harsh wind, wave and current conditions there is a growing need for monitoring and decision support, as well as remote and autonomous operations tied to transport, put out, feeding, sorting, delousing, treatment and slaughtering of the fish. The cost of having to interrupt such operations is substantial.
Historical data from the EXPOSED Aquaculture SFI, in which NTNU IDI is a partner, (GPS position of vessels, time at net cage, wind information, distance to protective geography, time in different zones, interrupted operations, etc.), and NCE Seafood Innovation Cluster (AquaCloud) are available for research and innovation. Within EXPOSED there is ongoing work utilizing some of these data.
The topic for the project work and master thesis is to predict potential problems tied to transport, put out, feeding, sorting, delousing, treatment and slaughtering operations in exposed areas of fish farming based on available historical data (from various sensors, measurements of food consumption and growth, etc.). In addition, the analysis of videos showing fish behaviour should be considered. Collaboration with companies developing drones for collecting data in fish farms could be considered.
The thesis will be done at the Telenor-NTNU AI-Lab in connection with the EXPOSED Centre (dealing with monitoring and decision support systems) and NCE Seafood Innovation Cluster.
Supervisor: Kerstin Bach, NTNU will be main supervisor, with me (Agnar Aamodt) as an assitant on the side, given that I will be away on sabbatical in the coming academic year.
Co-supervisors: Sigmund Akselsen, Telenor, og Gunnar Senneseth, Sintef Ocean