An IoT-Enabled Intelligent Monitoring System for Sustainable Aquaculture

Aquaculture plays a crucial role in ensuring global food security; however, it continues to face persistent challenges, including water quality degradation, delayed disease detection, and inefficient manual monitoring practices. To address these limitations, this study presents an intelligent aquaculture monitoring system that integrates Internet of Things (IoT) and Artificial Intelligence (AI) technologies to enable real-time, data-driven management of aquaculture environments. The system architecture combines ESP32-CAM microcontrollers equipped with temperature and pH sensors for continuous environmental monitoring and a mobile application for visualization and alerts. A lightweight YOLOv8n model was trained on a publicly available fish disease dataset to automatically detect six common fish diseases, achieving an overall mean Average Precision (mAP@0.5) of 0.983, with balanced precision and recall across all categories. Experimental results demonstrate the system’s ability to provide timely alerts, reliable sensing, and accurate disease classification in resource-constrained environments. By uniting IoT-based sensing with AI-driven diagnostics, the proposed system enhances operational efficiency, supports early intervention, and contributes to more sustainable and resilient aquaculture practices.