A Hybrid Method Monitors Water Quality to Detect Fish Diseases Early

This study explores a hybrid monitoring system designed to enhance early detection of fish diseases by continuously assessing water quality parameters in aquaculture environments. The research addresses the question of how integrating physical sensors with predictive analytics can improve disease prevention and minimize fish mortality. A combination of physical sensors for pH, temperature, and dissolved oxygen with machine learning algorithms enables real-time data collection and predictive analysis, identifying anomalies that signal potential disease outbreaks. Key findings show that the hybrid system detects early disease indicators with high accuracy, offering timely interventions that maintain healthier aquatic ecosystems. This approach demonstrates significant potential to improve aquaculture sustainability through proactive disease management.