Development of Smart Monitoring Technology in Floating Net Cages to Support sustainable Aquaculture

Abstract

The advancement of digital technology has accelerated the transformation of aquaculture management, including floating net cage (FNC) systems that operate under dynamic open-water conditions. This review synthesizes validated literature on smart monitoring technologies for supporting sustainable FNC aquaculture, with emphasis on Internet of Things (IoT)-based water quality monitoring, automated feeding, machine learning applications, and edge-computing integration. Following a systematic literature review procedure guided by PRISMA, the initial reference set was screened and corrected through reference validation, and only traceable and thematically relevant sources were retained for the final synthesis. The reviewed studies indicate that IoT-based systems improve real-time observation of key water quality parameters, including temperature, dissolved oxygen, pH, salinity, turbidity, and ammonia, thereby supporting faster operational responses. Automated feeding technologies can improve feed management and reduce organic waste, while machine learning and edge computing support the transition from descriptive monitoring to predictive and adaptive aquaculture management. However, field implementation remains constrained by sensor calibration, biofouling, power supply, network stability, limited long-term validation, and user capacity. Overall, smart monitoring technologies have strong potential to improve productivity and environmental sustainability in FNC aquaculture, but wider adoption requires robust field-adapted designs, validated sensor protocols, interoperable platforms, and farmer-oriented capacity building.