Edge-enabled distributed digital twins with embedded intelligence for smart aquaculture systems

Aquaculture is the world’s fastest-growing food production sector, yet it lags behind other industries in adopting upcoming digital technologies. Challenges, such as integrating multimodal data and maintaining reliable network connectivity, have hindered the development of digital twins for monitoring aquaculture systems. This paper addresses these challenges through two main contributions: (i) a novel edge-based architecture for digital twinning that enables distributed, localized monitoring and actuation, reducing dependence on centralized systems and robust networks; and (ii) a three-stage algorithmic approach for mortality monitoring tailored to edge computing environments. This approach enables early detection of rising mortality rates using data fused from diverse sources, including directly monitored environmental parameters (e.g. pH and temperature), and novel optical biosensors that make use of lightweight computer vision and machine learning techniques for the estimation of bacterial concentrations within edge devices. The algorithmic strategy was tested in a real-world recirculating aquaculture system for Solea senegalensis , where bacterial concentration was estimated with an F1-score of 0.83 across five concentration levels using biosensor imagery. Moreover, a multimodal drift detection algorithm successfully identified abnormal data trends aligned with significant changes in input distributions, with preemptive drift signals preceding critical 7-day mortality spikes.