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

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 perfo...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 37960 - 24
Hlavní autoři: Elmessery, Wael M., Shams, Mahmoud Y., El-Hafeez, Tarek Abd, Szűcs, Péter, Eid, Mohamed Hamdy, Alhumedi, M., Ahmed, Atef Fathy, Elwakeel, Abdallah Elshawadfy
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 30.10.2025
Nature Publishing Group
Nature Portfolio
Témata:
704/172
704/829
704/844
Agriculture
Algorithms
Aquaculture
Aquaculture - methods
Architecture
Control algorithms
Data collection
Decision making
Design
Economic analysis
Edge computing
Embedded systems
Humanities and Social Sciences
Infrastructure
Internet of Things
Lightweight DDPG
multidisciplinary
Neural networks
Neural Networks, Computer
Optimization
Real-time control
Recirculating aquaculture systems
Resource optimization
Science
Science (multidisciplinary)
Systems stability
Water quality
Resource optimization
Embedded systems
Precision aquaculture
Real-time control
Lightweight DDPG
Recirculating aquaculture systems
IoT deployment
Edge computing
ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-21677-0