A System for Monitoring Water Quality at Offshore Aquaculture Cages

Offshore aquaculture relies heavily on stable water conditions, but open-sea environments can change rapidly due to tides, weather variations, and pollution. These fluctuations can negatively impact fish health and lead to economic losses. To overcome this challenge, this project proposes an AI-enabled Buoy System that continuously monitors essential water-quality parameters using a Raspberry Pi 3B+ and a set of sensors including pH, turbidity, temperature, dissolved oxygen, and salinity. The collected data is processed locally, stored on an SD card, and displayed in real time on an LCD module. The processed readings are simultaneously transmitted to an IoT dashboard, allowing remote monitoring and early detection of abnormal water conditions. Artificial Intelligence algorithms can analyze trends and predict potential risks, enabling timely preventive action. By combining sensor technologies, wireless communication, and AI-based decision support, the proposed system provides a reliable, low-cost, and automated solution for improving the safety, productivity, and sustainability of offshore aquaculture operations.