Automated Fish Population Monitoring for Ecosystem Health Assessment: A Sonar-Based Machine Learning Approach

Evaluating the health of aquatic ecosystems depends greatly on precise monitoring of fish populations, a task that has traditionally relied on labor-intensive manual surveys with limited temporal coverage. This study introduces an innovative automated monitoring framework that combines sonar-based acoustic sensing with machine learning techniques to enable continuous tracking of fish populations in both freshwater and marine habitats. The system combines hydroacoustic sensing, advanced signal processing, and deep learning models to provide real-time estimates of fish abundance, species classification, and behavioural patterns. During a six-month deployment in estuarine environments, it achieved 87% detection accuracy and 79% biomass estimation accuracy when compared with traditional survey techniques. Operating continuously, the system reduces human bias and delivers high-resolution temporal data that are essential for understanding ecosystem dynamics. Results further demonstrate strong correlations between automated population metrics and water quality parameters, highlighting the system’s effectiveness in evaluating ecosystem health. Ultimately, this research contributes to marine conservation by presenting scalable, cost-effective solutions for biodiversity monitoring and environmental impact assessment.