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Transformative artificial intelligence integration in aquatic supply chains: synergizing precision aquaculture with
Xiaonan Fan1, Jiyu Zou1, Yang Liu2
1School of Management, Dalian Polytechnic University, Dalian 116034, Liaoning, China.
Food Chemistry: X
|April 22, 2026
Summary
Artificial intelligence (AI) can enhance aquatic supply chains from farm to table. While AI excels in tasks like water quality prediction, further development is needed for broader applications to ensure food safety and efficiency.
Area of Science:
- Aquatic supply chain management
- Artificial intelligence in food systems
- Sustainable aquaculture and fisheries
Background:
- Aquatic supply chains face significant sustainability and efficiency issues, including overexploitation and cold chain vulnerabilities.
- Existing research on artificial intelligence (AI) applications in aquatic supply chains lacks a comprehensive 'Farm-to-Table' synthesis.
- The growing global demand for aquatic products necessitates innovative solutions for safety and efficiency.
Purpose of the Study:
- To review and evaluate the integration of AI, specifically machine learning and deep learning, across the entire aquatic supply chain.
- To identify current applications, limitations, and future directions for AI in aquaculture, harvesting, processing, logistics, and marketing.
- To provide a theoretical foundation for enhancing product safety and operational efficiency in the aquatic industry.
Main Methods:
- Systematic literature review focusing on AI (machine learning, deep learning) applications in aquatic supply chains.
- Analysis of AI integration across key stages: aquaculture, harvesting, processing, logistics, and marketing.
- Evaluation of AI's efficacy in precision tasks versus nascent applications.
Main Results:
- AI demonstrates significant potential in precision tasks such as dynamic water quality prediction and automated catch classification.
- Applications of AI in pre-processing and low-altitude delivery stages of the aquatic supply chain are currently underdeveloped.
- The review highlights a gap in research synthesizing AI across the complete 'Farm-to-Table' continuum.
Conclusions:
- Interpretable algorithms, standardized databases, interdisciplinary collaboration, and cost-effective deployment are crucial for future AI advancements.
- Successful AI integration can lead to resilient, intelligent aquatic supply chains, improving food safety and meeting market demands.
- This review establishes a foundation for the digital transformation of the aquatic industry, promoting sustainability and efficiency.
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