Overview of Aquaculture Artificial Intelligence (AAI) Applications: Enhance Sustainability and Productivity, Reduce Labor Costs, and Increase the Quality of Aquatic Products
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
Authors
- Ehab El‐Haroun (ORCID: https://orcid.org/0000-0002-2848-2561)
- Mohamed Ashour (ORCID: https://orcid.org/0000-0002-1595-1197)
- Hien Van Doan (ORCID: https://orcid.org/0000-0002-7299-7087)
- Seyed Hossein Hoseinifar (ORCID: https://orcid.org/0000-0002-0210-9013)
- Waldemar Rossi (ORCID: https://orcid.org/0000-0003-4199-2424)
- Simon John Davies
- Sherine Ragab
Institutions
- Cairo University (EG)
- Ollscoil na Gaillimhe – University of Galway (IE)
- National Institute of Oceanography and Fisheries (EG)
- Chiang Mai University (TH)
- Gorgan University of Agricultural Sciences and Natural Resources (IR)
- Kentucky State University (US)
Publication Details
- Journal
- Annals of Animal Science
- Published
- 2024-08-29
- DOI
- https://doi.org/10.2478/aoas-2024-0075
- Citations
- 16
- Primary Topic
- Water Quality Monitoring Technologies
- Type
- article
- Field-Weighted Citation Impact
- 3.23
Funders
- Chiang Mai University