Aquaculture Monitoring: Intelligent Sensing and Control for Maximum Yield

Aquaculture is vital for global food security, yet conventional monitoring and control methods often result in poor water quality, inefficient resource utilization, and limited yield. This paper presents an AI-driven aquaculture monitoring and control system that integrates Edge AI-based anomaly detection, reinforcement learning (DQN) adaptive control, and a hybrid LoRaWAN/5G-IoT communication framework. A multi-parameter sensor network continuously tracks dissolved oxygen, pH, ammonia, turbidity, and temperature, with Edge AI enabling real-time anomaly detection and rapid corrective actions. Reinforcement learning dynamically optimizes feeding, aeration, and water exchange, while the hybrid communication model ensures reliable, low-latency, and energy-efficient data transfer. Experimental results demonstrate a 75% reduction in system response time, 62.5% improvement in dissolved oxygen stability, 22% higher feed conversion efficiency, 35% reduction in energy consumption, and up to 30% yield improvement over conventional and recent AI-based approaches. Automated feeding lowered feed waste by 70%, and intelligent aeration delivered significant power savings. The proposed framework provides a scalable, cost-effective, and sustainable solution to enhance productivity while reducing operational costs and environmental impact. Future extensions include blockchain-based traceability, drone-assisted monitoring, and offshore aquaculture deployment, advancing the next generation of smart aquaculture technologies.