A comprehensive review of machine learning applications in tilapia aquaculture

Jam Cyrex De Villa Delfino *, Daniel James Bañez Manaloto and Edwin Romeroso Arboleda

Department of Computer, Electronics, and Electrical Engineering, College of Engineering and Information Technology, Cavite State University, Cavite, Philippines.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 3008–3013.
Article DOI: 10.30574/ijsra.2024.12.1.1155

 

Publication history: 
Received on 14 May 2024; revised on 25 June 2024; accepted on 28 June 2024
 
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.
 
Keywords: 
Tilapia Aquaculture; Machine Learning; Disease Management; Water Quality Control; Feed Optimization; Predictive Modeling
 
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Copyright information: 

Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0