<![CDATA[The rapid expansion of high-density catfish aquaculture has increased the demand for efficient and precise feed management systems to optimize growth performance, reduce feed waste, and maintain water quality. Conventional feeding practices largely depend on fixed schedules and visual estimation, which often result in overfeeding or underfeeding, leading to increased production costs and environmental degradation. Recent advances in sensing technologies and artificial intelligence offer new opportunities to transform aquaculture management through data-driven and automated approaches. The purpose of this study is to develop and evaluate an automated feed management system for high-density catfish aquaculture by integrating acoustic sensors and machine learning algorithms. The system aims to accurately detect feeding activity and dynamically regulate feed delivery based on real-time fish behavior. This study employed a research and development design combined with experimental field testing. Acoustic sensors were deployed in catfish ponds to capture underwater sound patterns associated with feeding behavior. The collected acoustic data were processed using machine learning models to classify feeding intensity and determine optimal feeding duration. System performance was evaluated through accuracy testing, feed efficiency analysis, and comparative assessment against conventional feeding methods. The results show that the proposed system successfully identified feeding activity with high classification accuracy and significantly reduced feed waste compared to manual feeding practices. Feed conversion ratios improved, and water quality indicators remained more stable due to reduced excess feed accumulation. In conclusion, the automated feed management system demonstrates strong potential as an intelligent aquaculture solution for high-density catfish farming. By integrating acoustic sensing and machine learning, the system enhances feeding precision, supports sustainable aquaculture practices, and contributes to increased productivity and environmental efficiency.]]>
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