ReviewFood chemistry: X2024
Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand.
Review in Food chemistry: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Application of Artificial Intelligence in Aquaculture, Processing, Safety, and Traceability in the Industry of Aquatic Products: A Review.Foods (Basel, Switzerland) · 2026Review
- HIDANet: a lightweight deep learning framework for Vannamei post-larval stage classification and morphometric estimation with background bias validation.Frontiers in artificial intelligence · 2026Article
- Substrate Protein Enrichment of Black Soldier Fly (Aquaculture nutrition · 2026Article
- Host-Parasite Interactions and Integrated Management Strategies for Ecytonucleospora Hepatopenaei Infection in Shrimp.Acta parasitologica · 2025Review
- IMC-YOLO: a detection model for assisted razor clam fishing in the mudflat environment.PeerJ. Computer science · 2025Article
- AI-Driven Realtime Monitoring of Early Indicators for Ichthyophthirius multifiliis Infection of Rainbow Trout.Journal of fish diseases · 2025Article
- Review
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
No grant is acknowledged in the PubMed record.
Abstract
The increasing global population drives a rising demand for food, particularly fish as a preferred protein source, straining capture fisheries. Overfishing has depleted wild stocks, emphasizing the need for advanced aquaculture technologies. Unlike agriculture, aquaculture has not seen substantial technological advancements. Artificial Intelligence (AI) tools like Internet of Things (IoT), machine learning, cameras, and algorithms offer solutions to reduce human intervention, enhance productivity, and monitor fish health, feed optimization, and water resource management. However, challenges such as data collection, standardization, model accuracy, interpretability, and integration with existing aquaculture systems persist. This review explores the adoption of AI techniques and tools to advance the aquaculture industry and bridge the gap between food supply and demand.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.