Maintaining optimal water quality is fundamental to aquaculture productivity and sustainability. Conventional practices rely on manual sampling or static rule-based systems, which are labor-intensive, reactive, and often delayed in responding to dynamic environmental changes. Key water quality parameters—dissolved oxygen (DO), pH, temperature, biochemical oxygen demand (BOD), and coliform counts—must be continuously managed to safeguard aquatic health and ensure high yields.
This work proposes a reinforcement learning (RL)-based predictive control framework for real-time monitoring and adaptive management of aquaculture water quality. By framing water management as a sequential decision-making problem, the system learns optimal interventions such as aerator activation, water flow adjustment, or chemical dosing. A Double Deep Q-Network (DDQN) agent was employed to overcome overestimation bias and enhance learning stability. The model continuously receives feedback from water quality sensors, evaluates states, and recommends corrective actions to maintain parameters within optimal ranges while minimizing energy consumption.
Key contributions include:
- Integration of RL-driven adaptive control with real-time water quality monitoring.
- Use of DDQN agents for stable policy learning and improved decision-making.
- Demonstrated ability to maintain DO levels within safe ranges for ~90% of cycles, while reducing aerator energy usage by ~25%.
- Comparative results show superior adaptability, responsiveness, and efficiency compared to manual and rule-based systems.
This RL-driven predictive control approach establishes a scalable and intelligent foundation for sustainable aquaculture water management, reducing operational costs and supporting India’s Blue Transformation vision through AI-enabled environmental resilience.