Sustainable Aquaculture Management through IoT and Deep Learning-Driven Remote Monitoring

Aquaculture, as a critical component of global food security, faces challenges in maintaining optimal conditions for aquatic organisms. The paper presents an innovative IoT and Deep Learning-based remote monitoring system designed to enhance sustainable aquaculture management. In order to monitor water quality factors including temperature, pH, dissolved oxygen, and feeding behavior in real time, the system incorporates a network of sensors. These sensors’ data are locally processed on edge devices before being sent to a cloud-based platform for centralized storage and analysis. Convolutional neural networks (CNNs), in particular, are used in deep learning algorithms for image identification tasks to track the health and activity of fish. The system utilizes YOLOv8 for real-time detection and classification of fish diseases, ensuring early intervention and minimizing losses. User-friendly interfaces provide actionable insights through smart dashboards displaying trends, alerts, and recommended actions based on predictive analytics. By facilitating proactive management decisions, this IoT and Deep Learning-based system aims to optimize resource utilization, improve productivity, and promote sustainable practices in aquaculture. Case studies demonstrate its efficacy in enhancing operational efficiency, reducing environmental impact, and fostering long-term viability in aquatic food production systems.