Lightweight deep deterministic policy gradient for edge computing in recirculating aquaculture systems: real-time feeding control with reduced computational requirements

作者：Wael M. Elmessery, Mahmoud Y. Shams, Tarek Abd El‐Hafeez, Péter Szűcs, Mohamed Hamdy Eid, M. Alhumedi, Atef Fathy Ahmed, Abdallah Elshawadfy Elwakeel · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-21677-0 · 被引用次数：12 · 研究领域：Water Quality Monitoring Technologies、Innovations in Aquaponics and Hydroponics Systems、Smart Agriculture and AI

The deployment of advanced reinforcement learning algorithms in edge computing environments presents significant challenges for real-time aquaculture management, particularly in resource-constrained recirculating aquaculture systems (RAS). Building upon our previous work demonstrating superior performance of DDPG controllers in commercial RAS operations, this research introduces a lightweight DDPG architecture specifically optimized for edge computing deployment in recirculating aquaculture systems. The Edge-DDPG framework reduces computational complexity by 85% while maintaining 92% of the original model's performance accuracy. The lightweight architecture employs compact neural networks with reduced layer dimensions (64→32→1 neurons vs. 400→300→1 in the original), memory-efficient replay buffers (5,000 vs. 100,000 capacity), and CPU-optimized operations suitable for ARM-based edge devices. Experimental validation demonstrates consistent performance with average inference times of 15.2 ± 3.1 ms on Raspberry Pi 4B, enabling real-time control within 50 ms system response requirements. The edge-optimized controller achieved 94.3% feeding accuracy and 96.1% water quality stability while consuming only 47 ± 8 MB of system memory. Economic analysis demonstrates deployment cost reductions from $56,900 to $8,400 for large-scale implementations, enabling widespread adoption of intelligent feeding control in small to medium-scale aquaculture operations.