Drone-Based IoT System for Spatial Monitoring of Pond Water Quality for Precision Aquaculture

Aquaculture is an important source of food, employment and economic development, but maintaining appropriate water quality remains a major challenge for farmers. Parameters like temperature, pH, dissolved oxygen (DO), turbidity and salinity have significant effects on the growth, health, feeding efficiency and survival of aquatic organisms. Traditional monitoring relies mainly on manual sampling and periodic measurements, which is time-consuming and may not provide sufficient spatial and temporal information for precision aquaculture. This paper proposes an IoT-based mobile water-quality monitoring architecture with an unmanned aerial vehicle UAV (drone) integrated with an autonomous autopilot system. The system receives the registered GPS coordinates of an aquaculture pond and automatically navigates the UAV to the pond for measurement. When the UAV arrives at the pond, it visits several preselected sites and submerges a detachable multi-parameter sensing module to collect the water-quality parameters. An embedded controller processes the sensor data and transmits the measurements, GPS coordinates and time stamps to a cloud database via wireless communication. One mobile sensing unit can monitor multiple ponds and form a spatially indexed historical database of water quality. The architecture lays a foundation for AI-based anomaly detection, water-quality prediction, disease-risk assessment, and intelligent decision support for precision aquaculture.