Round Corner
Department of Computer and Information Science


AI-based prediction in aquaculture

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.), Norsk marint datasenter at Havforskningsinstituttet, BarentsWatch and NCE Seafood Innovation Cluster (AquaCloud) are available for research and innovation. Within EXPOSED there is ongoing work utilizing some of these data.

Within the aquaculture industries, there is a growing demand using AI and Machine Learning for predicting operational windows for maintenance, the spread of disease or defects on the sides. As the operations are usually costly and demand specifically-skilled personnel, planning those is key.

At NTNU and the AI Lab we have one SFI (Exposed) and additional research collaborations (e.g. with the NCE Seafood Innovation Cluster) in which different data set are available. With this theses one of the data sets should be utilised to developed a novel concept around ML/AI. We would like to involve the student(s) who is/are working on the project to define more detailed tasks, but some examples are:

  • Outbreaks of salmon lice in fish farms are a main cause for stress, reduced growth and death in commercial fish farming. Such attacks have been estimated to account for losses in billions of NOKs each year in Norwegian farms. Even a minor reduction in outbreaks would result in high profitability gains.
  • 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.).
  • Analysis of videos showing fish behaviour

The thesis will be conducted 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 and Agnar Aamodt, NTNU
Co-supervisors: Sigmund Akselsen, Telenor


Kerstin Bach Kerstin Bach
Associate Professor
312 IT-bygget
735 97410 
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