Abstract: In this paper, we describe about the design of an IoT-enabled, AI-driven water quality monitoring and control system for aquaculture. Maintaining optimal water conditions is essential for healthy aquatic ecosystems and sustainable food production. The system uses sensors to measure key parameters such as temperature, pH, turbidity, total dissolved solids (TDS), and ammonia levels. These sensors are integrated with Arduino Uno and ESP32 microcontrollers, which collect and transmit real-time data to a cloud platform for storage, visualization, and remote access. An AI model analyzes the data to detect anomalies, identify trends, and predict potential water quality issues. When abnormal conditions occur, the system generates instant alerts and classifies water quality into safe, warning, or critical levels using visual indicators. This enables faster decision-making and reduces the need for manual monitoring. Overall, the system improves efficiency, supports sustainable aquaculture practices, and helps ensure better environmental and production outcomes.

Keywords: IoT, Aquaculture, Water Quality Monitoring, Artificial Intelligence, Real-Time Monitoring.

Downloads: | DOI: 10.17148/IARJSET.2026.13506