From Traditional Machine Learning Models to Multimodal Large Models: A Review of Aquaculture

ABSTRACT With the rapid development of the global aquaculture industry, smart aquaculture has become increasingly essential for maintaining a stable supply of aquatic products. Traditional machine learning models frequently exhibit limited semantic understanding and insufficient scalability in complex, high‐dimensional environments. In contrast, multimodal large models integrate multi‐source information, significantly enhancing semantic depth, environmental adaptability, and natural interaction. This review explores recent research progress and future application prospects in the transition from traditional machine learning models to multimodal large models in aquaculture. The literature search was conducted using the Web of Science, Scopus, and Google Scholar databases. Based on literature from the past decade, this review summarises the evolution from traditional machine learning models to multimodal large models, emphasizing their recent advantages in integrating multi‐source data such as images, sensor readings, videos, audio, and text. By analyzing the current status and limitations of traditional machine learning in water quality monitoring, biomass estimation, behavior analysis, and health assessment, this review offers a comparative perspective on the potential of multimodal large models in critical aquaculture domains including sustainable water management, precision feeding, abnormal behavior detection, disease diagnosis and prevention, intelligent breeding, energy management, and embodied intelligent robotics. Multimodal large models still face several challenges, including data acquisition in aquaculture, enhancement of model performance, security and data governance in digital twin systems, and the advancement of industrial technologies. The review suggests that multimodal large models are poised to become key technological enablers in advancing aquaculture toward greater precision, sustainability, and automation.