What does this research mean for the field?

Artificial intelligence technologies, including deep learning and IoT, significantly enhance monitoring, management, and sustainability in aquaculture, but their commercial adoption is currently limited by scalability and interoperability challenges. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review current and emerging artificial intelligence applications across aquaculture and fisheries, integrating precision breeding, environmental tracking, and health management while analyzing barriers to commercial deployment.

September 3, 2026All LifeOpen Access

Artificial intelligence in aquaculture and fisheries: from precision breeding to environmental monitoring and intelligent health management

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Authors

MKM. S. KhanIslamia University of Bahawalpur AGAbdul GhaffarUniversity of Lahore MMMuhammad Asim Khan MahsudCOMSATS University Islamabad

Discussion

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Implication

Review demonstrates the role of artificial intelligence in optimizing aquatic food production, highlighting barriers to commercial scalability.

Key Points

To review current and emerging artificial intelligence applications across aquaculture and fisheries, integrating precision breeding, environmental tracking, and health management while analyzing barriers to commercial deployment.
Synthesized research literature on artificial intelligence tools, deep learning models, the Internet of Things, and blockchain across aquatic food production systems.
Evaluated challenges regarding commercial translation, focusing on scalability, interoperability, and adoption gaps in aquaculture operations.
AI-driven frameworks and deep learning tools enhance operational monitoring, decision-making, and supply chain traceability while substantially lowering labor costs.
Commercial adoption remains constrained by limited land availability, aquatic pollution, and technical barriers in scaling AI prototypes.
Future implementation depends on integrating cloud computing, automated decision-support systems, and robotics to improve commercial viability.

Cite This Study

Khan et al. (2026) studied this question.

synapsesocial.com/papers/6a9934cf636c6408cfa7c895 https://doi.org/10.1080/26895293.2026.2723275

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Implication

Review demonstrates the role of artificial intelligence in optimizing aquatic food production, highlighting barriers to commercial scalability.

Artificial intelligence in aquaculture and fisheries: from precision breeding to environmental monitoring and intelligent health management

Discussion

Member takes

Implication

Key Points

Cite This Study

Artificial intelligence in aquaculture and fisheries: from precision breeding to environmental monitoring and intelligent health management

Discussion

Member takes

Implication

Key Points

Cite This Study

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