Abstract

Constraints on natural resources and global warming have made premium seafood a global issue in modern culture. By implementing fish farming IoT technologies, seafood production can increase significantly by optimizing resource consumption and improving fish growth rates. Such objectives require control, measurement, and monitoring of parameters like temperature, pH, water level, and feeding rate, along with fish growth structure. Proper farming of fish is crucial to global food production; hence, suitable water parameters are fundamental for the development and wellbeing of aquatic organisms. This work offers a flexible, efficient method by using a precise water quality monitoring and maintenance system (PWQM&M) for AF pools. This work is distinguished by using fuzzy logic to AF systems aiming at improving the management and boost accuracy to overcome the system rigidity of conventional fuzzy-based control systems. By doing so, superior performance is achieved in terms of autonomy, responsiveness, and speed while facilitating dynamic Water Quality (WQ) with no human input. WQ monitoring adds value to increasing system performance; controlling AF systems with such complexity enables local WQM manipulation on the diverse parameters of AF environments. An operation test of 48 hours at which appropriate particle levels of oxygen and salt are maintained demonstrates the efficacy for the purpose. This demonstrates the system's effectiveness and their performance in real-world scenarios.

Keywords: Internet of things, Aquatic farming, Fuzzy logic, Water quality, Monitoring, dissolved oxygen.