Evaluation of Intelligent Aquaculture Monitoring Systems Using IoT

Abstract

The accelerating expansion of aquaculture as a primary source of animal protein has intensified the need for intelligent monitoring solutions capable of ensuring productivity, sustainability, and environmental resilience. Conventional aquaculture management relies on periodic manual sampling and visual inspection, which fail to capture rapid fluctuations in water quality and often result in delayed corrective actions. This study presents an extensive evaluation of intelligent aquaculture monitoring systems based on Internet of Things technologies, focusing on architectural design, performance efficiency, operational reliability, scalability, and deployment feasibility. The proposed evaluation framework integrates multi-parameter water quality sensors, low-power wireless communication, cloud-based analytics, and automated alert mechanisms. Comparative experimental analysis with traditional monitoring practices is conducted to assess data accuracy, latency, energy consumption, fault tolerance, and economic viability. Results demonstrate that IoT-enabled systems substantially enhance early anomaly detection, reduce resource wastage, and support predictive aquaculture management. However, challenges such as sensor degradation, connectivity limitations, cybersecurity vulnerabilities, and maintenance costs persist, particularly in resource-constrained regions. The study concludes that intelligent IoT-based aquaculture monitoring systems offer a transformative pathway toward precision aquaculture, provided that technological, economic, and policy-level barriers are systematically addressed.