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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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 37960 - 24
Hauptverfasser:	Elmessery, Wael M., Shams, Mahmoud Y., El-Hafeez, Tarek Abd, Szűcs, Péter, Eid, Mohamed Hamdy, Alhumedi, M., Ahmed, Atef Fathy, Elwakeel, Abdallah Elshawadfy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 30.10.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	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
Online-Zugang:	Volltext
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