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RACE Digital Cage

A digital platform for monitoring and visualisation of aquaculture net cages and fish distribution based on sensors, real-time simulations and data assimilation (theoretical, mathematical models combined with observations).

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There is a need for the fish farming industry to evolve from relying on experience-based manual observations to knowledge-based data-driven reasoning by applying the principles of Precision Fish Farming. This will lead to more precise production and operation planning and give improved control of fish welfare and technical integrity of the structure.

DigitalCage aims to develop a generalised interface for collection, storage, interpretation and visualisation of different monitoring sensor data and an integrated framework for modelling and simulation of net cage deformation and fish distribution with data assimilation.

A possible solution for the continuous monitoring of net cage deformation and fish distribution would be to incorporate the instantaneous environmental sensor data into the numerical simulation model, and use

  • accelerometers for measuring and estimating floating collar deformations
  • load shackles for determining mooring loads
  • pressure and/or acoustic sensors for measuring and estimating net deformations
  • echosounders for determining vertical distributions of the fish at different locations of the cage
  • video cameras for observing variations in individual fish size and behaviour

A generalised interface for data management and data assimilation will be developed, ensuring high quality useable data and optimally combining a large variety of data with knowledge-based predictive models.

Improvement in simulation speed is vital for real-time simulations. Complex scenarios and multiple models need to be simulated simultaneously at high speed to be able to present the results and online interpretation of the situation, which in turn will give important knowledge for operators and decision makers during operations.

DigitalCage is a step towards a full digital twin of a fish farm. The cross-discipline and multi-department project will develop and integrate available knowledge of sensor technology for fish cage monitoring, data management, numerical simulations and data assimilation. The outcome of this project will be integrated as part of a full-scale laboratories currently be developed in OceanLabs node 3.

Main objective:

Develop an integrated method that combines real-time numerical simulation models and multiple sensor data for the continuous monitoring and visualization of net cage conditions and fish distributions under environmental and operational variability.

Secondary objectives:

  • Study the existing sensor technologies for the continuous monitoring of net cage conditions and fish distributions, implement the selected sensor systems in field studies, and develop algorithms and a generalized interface for collection, storage, interpretation and visualization of sensor data.
  • Develop an integrated framework for modelling and simulation of an actual fish and cage system with data assimilation, validate the implemented numerical models and estimation methods through specialized case studies, and evaluate the performance of the integrated framework.
  • Demonstrate the feasibility of the entire system, and develop a knowledge foundation for future technologies relevant to digital twin, Precision Fish Farming (PFF) and virtual testing.
  • The developed monitoring and visualization framework will be used for analysis and visual representation of the data collected through the permanent setup in OceanLabs Node3.


Key Factors

Project duration

2020 - 2022

Budget: 5,1 MNOK