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Overview of Aquaculture Artificial Intelligence (AAI) Applications: Enhance Sustainability and Productivity, Reduce Labor Costs, and Increase the Quality of Aquatic Products Cover

Overview of Aquaculture Artificial Intelligence (AAI) Applications: Enhance Sustainability and Productivity, Reduce Labor Costs, and Increase the Quality of Aquatic Products

Open Access
|Apr 2025

Abstract

The current work investigates the prospective applications of Artificial Intelligence (AI) in the aquaculture industry. AI depends on collecting, validating, and analyzing data from several aspects using sensor readings, and feeding data sheets. AI is an essential tool that can monitor fish behavior and increase the resilience and quality of seafood products. Furthermore, AI algorithms can early detect potential pathogen infections and disease outbreaks, allowing aquaculture stakeholders to take timely preventive measures and subsequently make the proper decision in an appropriate time. AI algorithms can predict ecological conditions that should help aquaculture farmers adopt strategies and plans to avoid negative impacts on the fish farms and create an easy and safe environment for fish production. In addition, using AI aids to analyze and collect data regarding nutritional requirements, nutrient availability, and price could help the farmers to adjust and modify their diets to optimize feed formulations. Thus, using AI could help farmers to reduce labor costs, monitor aquatic animal’s growth, health, optimize feed formulation and reduce waste output and early detection of disease outbreaks. Overall, this review highlights the importance of using AI to achieve aquaculture sustainability and boost the net profits of farmers

DOI: https://doi.org/10.2478/aoas-2024-0075 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402
Language: English
Page range: 441 - 453
Submitted on: May 15, 2024
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Accepted on: Jul 1, 2024
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Published on: Apr 24, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2025 Sherine Ragab, Seyed Hossein Hoseinifar, Hien Van Doan, Waldemar Rossi, Simon Davies, Mohamed Ashour, Ehab El-Haroun, published by National Research Institute of Animal Production
This work is licensed under the Creative Commons Attribution 4.0 License.