Emerging Digital Technologies for Smart Aquaculture

Authors : Tolga Şahin

Pages : 188-194

View : 213 | Download : 1867

Publication Date : 2025-11-30

Article Type : Review Paper

Abstract :Digital technologies, as seen in all industries, have great potential to increase present benefits to an extended level. In this context, together with their apparent advantages, they have swiftly and continuously transformed the aquaculture industry into a more modern one in terms of productivity, resilience, and environmental sustainability. The present review, which is a narrative, is intended to address those improvements by reporting the latest empirical findings on the use of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), computer vision (CV), and robotic applications in aquaculture systems. According to the reviewed literature, IoT-based water quality monitoring has been demonstrated to cause improved growth rates, survival, and early detection of anomalies in farmed aquatic animals, while AI/ML algorithms, in parallel, predict changes in the levels of dissolved oxygen, incidences of disease risks, and selective breeding performances. Through non-invasive evaluation of respiration, behavior, and biomass, CV platforms facilitate comprehensive welfare monitoring, consequently supporting more precise feeding applications and better feed conversion ratios. Robotics and autonomous vehicles/tools, which carry out environmental surveys, fouling removal, and net inspections in offshore farms with very little human presence, lead to enhanced operability and vision. In spite of these developments, some issues such as sensor durability/robustness, higher implementation costs, system inter-operability restrictions, and limited transferability over various aquatic species to be farmed, are regretfully present. Hereby, in the future, biotechnology with digital twins, AI-supported early warning systems, and robotics powered by renewable energy seem to be the main path toward a more autonomous and intelligent aquaculture.
Keywords : Artificial intelligence, Computer vision, Iot, Machine learning, Robotics

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