Artificial intelligence in smart seafood safety across the supply chains: Recent advances and future prospects

文献类型： 外文期刊

第一作者： Jiao, Xidong

作者： Jiao, Xidong;Zhu, Jinlin;Ye, Weijian;Zou, Hao;Yan, Bowen;Zhang, Nana;Zhang, Hao;Fan, Daming;Zhu, Jinlin;Ye, Weijian;Zou, Hao;Yan, Bowen;Zhang, Nana;Zhang, Hao;Jiao, Xidong;Qiang, Jun;Tao, Yifan;Jiao, Xidong;Qiang, Jun;Tao, Yifan;Zhang, Dachuan;Zhu, Jinlin;Fan, Daming;Fan, Daming

作者机构：

关键词： Artificial intelligence; Seafood safety; Aquaculture; Freshness monitoring; Seafood adulteration

期刊名称：TRENDS IN FOOD SCIENCE & TECHNOLOGY （ 影响因子：15.4； 五年影响因子：18.4 ）

ISSN： 0924-2244

年卷期： 2025 年 163 卷

页码：

收录情况： SCI

摘要： Background: Aquatic blue foods are critical to global food security and nutrition, yet the sector faces growing challenges from food safety, supply chain complexity, and sustainability concerns. The integration of artificial intelligence (AI) technologies across the seafood supply chain offers transformative opportunities to enhance detection accuracy, ensure safety, improve efficiency, and promote sustainable development. Scope and approach: This review systematically summarizes recent advances in AI-enabled intelligent safety monitoring and processing strategies spanning the entire aquatic food chain, from aquaculture production to post-harvest handling and retail. Key developments are highlighted in four major areas: (i) smart aquaculture systems for effective supply and resilience enhancement of seafood; (ii) AI-driven seafood freshness monitoring leveraging smart packaging, sensors, and computer vision technologies; (iii) automated pre-processing of fish products including species classification, morphometric estimation, and intelligent cutting systems; and (iv) AI-guided detection of seafood adulteration, such as species substitution, origin mislabeling, and frozen-thawed fraud. Finally, we discussed current challenges and offered perspectives on advancing AI-driven systems for seafood safety detection and traceability. Key findings and conclusions: AI is a crucial means to ensure the effective supply of aquatic products and maintain food safety. It has demonstrated superior capabilities in non-destructive sensing, multidimensional data processing, and real-time decision support. Despite significant progress, challenges such as model generalization, dataset standardization, and the development of field-deployable intelligent systems remain pressing. Future research directions include integrating AI with novel sensor platforms, developing robust multimodal data fusion techniques, and advancing predictive models to achieve resilient and sustainable aquatic food supply chains.

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