Abstract
The BOOST project aims to support the restoration of European flat oyster (Ostrea edulis) reefs in the North Sea by developing a method for large-scale reef restoration. This method focuses on BlueLinked’s ReefBooster in combination with automated observing techniques such as the autonomous underwater vehicle (AUV) developed by Lobster Robotics and artificial intelligence models developed by Wageningen University & Research. For underwater observations in the field, the use of AUVs like the Lobster Scout has improved large-scale reef imaging, offering more accurate data for assessments after deployment. AI-based spat detection models, particularly Cascade Mask R-CNN, have proven effective in tracking oyster spat in a hatchery setting. However, detecting ReefBoosters with spat once deployed on the seabed still requires more work for it to be applicable in the field. Furthermore, small design modifications to the ReefBooster, such as weight distribution, can significantly impact its functionality. With regards to material composition of the ReefBoosters, some results were inconclusive, but main observations suggest that calcium-rich materials, such as lime and seashells, promote oyster settlement and foster marine biodiversity. BlueLinked has demonstrated the ability to successfully cultivate flat oysters in their hatchery, from broodstock to new oyster spat. Despite challenges in assessing long-term stability, the ReefBooster method in combination with automated techniques such as an AUV and AI, is shown to be effective for nearshore restoration and offers promising scalability for large-scale projects.