A review of generative AI in aquaculture: Applications, case studies and challenges for smart and sustainable farming

Abstract

Generative Artificial Intelligence (GAI) is revolutionizing aquaculture by providing practical and scalable solutions to longstanding industry challenges, including limited data availability, labor-intensive underwater inspections, disease outbreaks, and inefficiencies in resource management. As the sector evolves toward the Aquaculture 4.0 vision of intelligent, interconnected, and sustainable systems, GAI offers transformative capabilities across perception, planning, optimization, and communication. GAI enhances automation, decision support, and situational awareness across the aquaculture value chain through the intelligent synthesis of multimodal data ranging from sensor logs and underwater imagery to textual records and simulations. This review presents the first comprehensive synthesis of GAI in aquaculture, covering foundational models (e.g., diffusion models, transformers, and GANs), domain-specific applications, and emerging deployment scenarios. We demonstrate how GAI drives industry innovation in areas such as ROV-based infrastructure inspection, digital twins for farm design, synthetic data generation for fish health diagnostics, multimodal sensor fusion, and personalized advisory systems. Importantly, we map GAI models to specific aquaculture tasks, highlighting their suitability and advantages. We also offer a critical assessment of their operational readiness, including trust, performance, and environmental impact issues. In addition, we provide a systematic classification of applications, case studies, and future directions to guide the responsible and scalable integration of GAI in aquaculture. This review highlights GAI as a powerful tool and a foundational enabler of innovative, resilient, and ecologically aligned aquaculture systems, accelerating the industry’s transition toward more efficient, transparent, and adaptive practices.

Topics & Concepts