Transformative artificial intelligence integration in aquatic supply chains: synergizing precision aquaculture with intelligent logistics and data-driven consumption.

Xiaonan Fan, Jiyu Zou, Yang Liu, Dongmei Li, Dayong Zhou, Hai Chi, Deyang Li

Abstract readReview

In one paragraph

Review in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xiaonan FanSchool of Management, Dalian Polytechnic University, Dalian 116034, Liaoning, China.

Jiyu ZouSchool of Management, Dalian Polytechnic University, Dalian 116034, Liaoning, China.

Yang LiuSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Key Laboratory for Marine Food Science and Technology, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Dongmei LiSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Key Laboratory for Marine Food Science and Technology, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Dayong ZhouSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Key Laboratory for Marine Food Science and Technology, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Hai ChiKey Laboratory of Protection and Utilization of Aquatic Germplasm Resource, Liaoning Ocean and Fisheries Science Research Institute, Dalian 116023, China.

Deyang LiSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing, Liaoning Province Key Laboratory for Marine Food Science and Technology, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aquatic supply chains currently encounter critical sustainability and efficiency challenges, ranging from resource overexploitation to inherent cold chain vulnerabilities. Although individual artificial intelligence (AI) applications are emerging, research synthesizing the entire "Farm-to-Table" continuum remains scarce. This review bridges this gap by evaluating AI integration-specifically machine learning and deep learning-across aquaculture, harvesting, processing, logistics, and marketing. The analysis reveals that while AI demonstrates notable efficacy in precision tasks like dynamic water quality prediction and automated catch classification, applications in pre-processing and low-altitude delivery remain nascent. Future advancements require interpretable algorithms, standardized databases, interdisciplinary collaboration, and cost-effective deployment to construct resilient, intelligent supply chains that ensure food safety and satisfy growing global market demands. Ultimately, this review provides a robust theoretical foundation for researchers and practitioners to enhance product safety and operational efficiency, fostering a sustainable, digital transformation of the aquatic industry.

Indexed as

Aquatic productsArtificial intelligenceDeep learningMachine learningQuality and safetySupply chain

Identifiers

PMID42016730

DOI10.1016/j.fochx.2026.103833

PMCPMC13092574

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.