Journal of ISMAC
IRO Journals
Volume 7 — Issue 1 — March 2025

EcoGuard: Advancing IoT-based Aquaculture with Machine Learning for Enhanced Productivity and Automation

https://doi.org/10.36548/jismac.2025.1.002
Pages: 18-41
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Jarin Nooder Esty
Computer Science and Engineering, Daffodil International University
Abu Salyh Muhammad Mussa
IoT and Robotics Engineering, Bangabandhu Sheikh Mujibur Rahman Digital University
Md. Fazle Rabbi
Electrical and Electronics Engineering, American International University
Md. Rakibul Hasan
Computer Science and Engineering, University of Skill Enrichment & Technology
Md. Golam Muhit
Computer Science and Engineering, East West University
Nafis Shahriar Munir
Electrical and Electronics Engineering, Louisiana State University (LSU),
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How to Cite

Esty, Jarin Nooder, Abu Salyh Muhammad Mussa, Md. Fazle Rabbi, Md. Rakibul Hasan, Md. Golam Muhit, and Nafis Shahriar Munir. 2025. “EcoGuard: Advancing IoT-Based Aquaculture With Machine Learning for Enhanced Productivity and Automation”. Journal of ISMAC 7 (1): 18-41. https://doi.org/10.36548/jismac.2025.1.002.
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Keywords

— EcoGuard
— IoT
— ML
— Automation
— Aquaculture
— Monitoring
— Fish Farming
— Fish Recommendation
— Water Management
— Water Cleaning
Published: 27-02-2025

Abstract

The increasing demand for sustainable aquaculture necessitates efficient water quality management to enhance fish health, reduce mortality rates, and improve overall productivity. However, conventional water quality monitoring relies on manual testing, which is labour-intensive, time-consuming, and ineffective in detecting rapid environmental fluctuations. To address these limitations, this study presents EcoGuard, an IoT-enabled smart aquaculture monitoring system that integrates edge computing and federated learning-based predictive analytics for real-time water quality assessment and management. EcoGuard continuously monitors the important water parameters, including pH, dissolved oxygen (DO), temperature, turbidity, and ammonia levels, through a wireless sensor network. The predictive analytics module, employing Random Forest and Long Short-Term Memory (LSTM) models, forecasts water quality trends, enabling early intervention and risk mitigation. A key feature of EcoGuard is its federated learning framework, which facilitates collaborative model training across multiple aquaculture farms while ensuring data privacy and security. The system utilizes the MQTT protocol for low-latency data transmission, while an integrated mobile application provides real-time alerts and decision support for optimized resource management. Experimental validation demonstrates that EcoGuard effectively reduces fish mortality, enhances operational efficiency, and supports sustainable aquaculture practices. By utilizing IoT, AI, and federated learning, the proposed system offers a scalable, cost-effective, and intelligent solution for modernizing aquaculture, contributing to food security, environmental conservation, and resilient fisheries management.

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