mlsanityData to 14 Oct 2026

AQUA: A Large Language Model for Aquaculture & Fisheries

arXiv 2507.20520 ↗First posted 28 Jul 2025cs.CL cs.AI cs.CEcited by 1

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Abstract (arXiv)

Aquaculture plays a vital role in global food security and coastal economies by providing sustainable protein sources. As the industry expands to meet rising demand, it faces growing challenges such as disease outbreaks, inefficient feeding practices, rising labor costs, logistical inefficiencies, and critical hatchery issues, including high mortality rates and poor water quality control. Although artificial intelligence has made significant progress, existing machine learning methods fall short of addressing the domain-specific complexities of aquaculture. To bridge this gap, we introduce AQUA, the first large language model (LLM) tailored for aquaculture, designed to support farmers, researchers, and industry practitioners. Central to this effort is AQUADAPT (Data Acquisition, Processing and Tuning), an Agentic Framework for generating and refining high-quality synthetic data using a combination of expert knowledge, largescale language models, and automated evaluation techniques. Our work lays the foundation for LLM-driven innovations in aquaculture research, advisory systems, and decision-making tools.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 28 Jul 2025.

Builds on

7 of 40 references.

References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2024Qwen2.5 Technical Reportcited by 4,942
  3. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  4. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  5. 2024Docling Technical Reportcited by 34
  6. 2020Deep learning for smart fish farming: applications, opportunities and challengescited by 12
  7. 2024A Comparative Study of PDF Parsing Tools Across Diverse Document Categoriescited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

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  1. 2025A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming
  2. 2025AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
  3. 2025FishNet++: Analyzing the capabilities of Multimodal Large Language Models in marine biology
  4. 2025Large Language Models for Water Distribution Systems Modeling and Decision-Making
  5. 2024Harnessing Large Vision and Language Models in Agriculture: A Review
  6. 2025AgriGPT: a Large Language Model Ecosystem for Agriculture
  7. 2025FishAI 2.0: Marine Fish Image Classification with Multi-modal Few-shot Learning
  8. 2024Benchmarking Large Language Models for Image Classification of Marine Mammals