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.
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:
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, og Gunnar Senneseth, Sintef Ocean