Abstract
Tilapia aquaculture has become a crucial segment of global fish production due to its economic viability and adaptability. However, the industry faces challenges in disease management, water quality control, and feed optimization. This comprehensive review examines the applications of machine learning (ML) in addressing these challenges within tilapia aquaculture. Key areas explored include disease detection and diagnosis, water quality monitoring, feed strategy optimization, and production management. The review highlights various machine learning models and methodologies employed, discusses their effectiveness, and identifies future directions for research and development. The findings suggest that while machine learning offers substantial potential for enhancing tilapia aquaculture, challenges such as data quality, integration, and scalability need to be addressed to fully realize these benefits.
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