Internet of Things: Water Quality Monitoring System Using Machine Learning (Fish Aquaculture)

Water quality is a critical factor in maintaining the health of aquatic ecosystems, particularly in fish ponds where imbalanced pH levels, high turbidity, and fluctuating temperatures can lead to disease outbreaks and reduced fish productivity. This project proposes an IoT-based smart water quality monitoring system integrated with machine learning to continuously assess and optimize water conditions. The system uses an Arduino microcontroller connected to pH, turbidity, temperature, and water level sensors to collect real-time data. An LCD displays the water parameters, while an automated liquid pump adjusts pH levels, and a turbine reduces turbidity when necessary. Data is transmitted to a cloud platform for remote monitoring and processed by a machine learning algorithm to predict water quality trends, ensuring proactive intervention. The proposed solution improves fish health, reduces human intervention, and enhances aquaculture efficiency through predictive analytics and automation.