ReviewFoods (Basel, Switzerland)2026
Application of Artificial Intelligence in Aquaculture, Processing, Safety, and Traceability in the Industry of Aquatic Products: A Review.
Review in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The aquatic products industry has experienced rapid development due to the growing global demand for high-quality proteins. However, conventional production models remain constrained by multifaceted impediments, including disease outbreaks, environmental stressors, microbial contamination, and operational inefficiencies in processing, quality control, and logistics. Artificial intelligence (AI) offers targeted methodological solutions to these challenges. From a functional perspective, this review categorizes artificial intelligence into four major types: perception, prediction, control, and generation, and systematically evaluates its application progress in aquaculture, processing, quality inspection, and traceability fields. Perception AI constitutes the data acquisition and digitization layer, utilizing computer vision, sonar, and multimodal fusion technologies to establish digital mappings from environmental parameters to biological indicators. Prediction AI employs machine learning and deep learning algorithms to transform historical datasets into quantitative forecasts regarding water quality dynamics, disease risks, and production trends. Control AI translates decision-making protocols into precise, autonomous regulatory actions for aquaculture environments and processing workflows through fuzzy logic and model-based predictive control. Generation AI leverages large language models and generative adversarial networks to demonstrate innovative capabilities in data augmentation, solution optimization, and virtual simulation. Collectively, these applications optimize core production processes while significantly enhancing product quality, processing efficiency, safety management, and traceability systems. Future research directions will prioritize the development of robust, interdisciplinary AI technologies with superior integration capabilities.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.