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Automation and artificial intelligence for data analysis and cultivation of low-trophic species

In SINTEF we focus on automated solutions based on artificial intelligence (AI), internet of things (IoT) and computer science to develop accurate-, predictable and profitable solutions for cultivation of low-trophic species and automated analysis of experiment-data.

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Machine vision (MV) is the use of digital imaging through dedicated hardware and software to extract information from an image. This may include quality parameters, size, shape or density, and use of machine learning. MV has great potential as sensing technique in many areas and is used across several institutes in SINTEF for a variety of problems. Aquatic bioresources utilise MV in dedicated instruments (e.g., algae density monitoring, zooplankton monitoring, seaweed quality and biomass measurements), and for automation tasks (e.g., fish feeding control, copepod culture control). MV has also been used to reduce labour-intensive monitoring of zooplankton behaviour experiments in lab trials.