Technological innovations are driving precision aquaculture towards a future of transparent, automated, and sustainably efficient production

Hijran  Yavuzcan; Abdallah Tageldein  Mansour

doi:10.64636/ar.37

Authors

Hijran Yavuzcan Department of Fisheries and Aquaculture, Faculty of Agriculture, University of Ankara, 06110 Ankara, Turkey https://orcid.org/0000-0001-6567-7467

Competing Interests

There is no conflict of interest to declare.
Abdallah Tageldein Mansour Animal and Fish Production, College of Agriculture and food Sciences, King Faisal University, Al Hofuf, Kingdom of Saudi Arabia; Fish and Animal Production Department, Faculty of Agriculture (Saba Basha), Alexandria University, Alexandria, 21531, Egypt https://orcid.org/0000-0002-5963-5276

Competing Interests

none The author declare that they have no competiing interests.

Corresponding Author

Abdallah Tageldein Mansour

Abdallah.Mansour.JAAR@alexu.edu.eg ; https://orcid.org/0000-0002-5963-5276

DOI:

https://doi.org/10.64636/ar.37

Abstract

Precision aquaculture (PA) is a major change in the fish farming industry because it incorporates Internet of Things (IoT), artificial intelligence (AI), automated technologies, and real-time solutions to monitor efficient intensive production and minimize the adverse effect timely. This review shows the existing condition of PA technologies, their use, advantages, problems, and future perspectives on its basis and according to recent research. Monitoring water quality with the use of IoT sensor networks, analyzing fish behavior with the use of the computer vision systems, automated feeding system, and AI-based decision support systems are the main technological elements of PA. Recent research shows that PA has been shown to offer considerable improvement in terms of feed efficiency (30% savings), fish survival (33% higher), predictive accuracy (R² = 0.94), and change in revenue (up to 15.51% in field trials). It also participates in reducing labor costs and energy consumption. The return on investment ranged from 1-3 years depending on farm scale (in favor of large-scale farm) and system complexity. However, some challenges remain, including sensor reliability, data standardization, high initial costs, and system integration. Future trends of PA point toward blockchain, digital twins, multimodal sensor fusion, autonomous robotics, and explainable AI systems, energy-harvesting systems that will further revolutionize aquaculture operations. This revolution will drive the shift from aquaculture 4 (technology-driven approach) to aquaculture 5 (ecological sustainability and system robustness systems and make aquaculture more transparent, automated, and sustainable.

Keywords:

Precession aquaculture, aquaculture 4, aquaculture revolution, Internet of Things, multimodal sensor, Artificial Intelligence, sustainable production

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