Artificial intelligence and digital innovations in precision aquaculture: Advancements, applications, and future directions

Искусственный интеллект и цифровые инновации в прецизионном аквакультуре: достижения, применения и перспективы
Rishikesh Ratan, Ashwini R M, Dibyendu Kamilya, Vishwanath Nagarajan
2026-03-10

AI in precision aquacultureIoT and Big Data integrationdigital twinsedge AI deploymentfederated learning
• Integration of AI, IoT, and Big Data is revolutionising automation and predictive control in aquaculture. • Advanced technologies, including federated learning, blockchain, and digital twins, enhance process optimisation. • Socioeconomic, infrastructural, and regulatory barriers impact large-scale AI implementation in aquaculture. The aquaculture sector is undergoing a rapid digital transformation, driven by the accelerated advancement of artificial intelligence (AI) technologies. This review examines how AI, alongside the Internet of Things (IoT) and Big Data, is reshaping smart aquafarming systems through intelligent automation, real-time decision-making, and predictive analytics. AI methodologies, such as machine learning, deep learning, attention-based models, and hybrid algorithms, are enabling significant improvements in water quality prediction, disease detection, feed optimisation, and behavioural analysis. The review also explores cutting-edge innovations beyond conventional AI applications. These include federated learning, explainable AI, edge AI deployment, and transfer learning, which address critical challenges related to data and system scalability in dynamic aquaculture environments. Particular emphasis is placed on the integration of AI with IoT systems and the development of digital twins to simulate and optimise aquaculture processes for predictive control. Furthermore, the potential of quantum AI and blockchain technologies are also assessed in the context of future-proof aquaculture systems. The review also provides a critical assessment of the socioeconomic barriers, infrastructural limitations, ethical considerations, and regulatory gaps that hinder the widespread implementation of AI—especially in rural and resource-constrained settings. This analysis presents a prospective framework for the development of scalable, resilient, and sustainable aquaculture systems empowered by next-generation AI technologies.
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Advanced approaches—federated learning, explainable AI, edge AI, and transfer learning—address data and scalability challenges in dynamic aquaculture environments.
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Integration of AI with IoT and development of digital twins enables simulation and optimization of aquaculture processes for predictive control.
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Integration of AI, IoT, and Big Data is revolutionizing automation, real-time decision-making, and predictive control in aquaculture.
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Machine learning, deep learning, attention-based models, and hybrid algorithms improve water quality prediction, disease detection, feed optimization, and behavioral analysis.
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Socioeconomic, infrastructural, ethical, and regulatory barriers, especially in rural and resource-constrained settings, hinder large-scale AI implementation; blockchain and quantum AI are assessed as potential future enablers.

Smart/precision aquaculture systems (AI-, IoT-, and Big Data-enabled aquafarming systems)

Integration and application of AI and digital innovations (machine learning, deep learning, federated learning, explainable AI, edge AI, digital twins, blockchain, quantum AI) for automation, real-time decision-making, predictive control, water quality prediction, disease detection, feed optimisation, behavioural analysis, and scalability/resilience challenges in aquaculture

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2026-03-10
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Rishikesh Ratan
Ashwini R M
Dibyendu Kamilya
Vishwanath Nagarajan
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