A Conceptual Framework for AI-Enabled System Modeling for Early Detection of Catastrophic Loss to Ensure Aquaculture System Sustainability

Aquaculture plays a crucial role in meeting the growing global demand for seafood. However, the sector faces significant challenges, including mass mortality events (MMEs) triggered by disease outbreaks, environmental shifts, and inefficient fish feeding practices. Early intervention is challenging because traditional monitoring and risk management techniques are primarily reactive and constrained by human capabilities. To enhance autonomous decision-making, adaptive management, and predictive risk assessment in aquaculture, this study proposes a Generative Artificial Intelligence (GenAI) framework. The conceptual framework can produce real-time insights, detect early warning indications of MMEs, and suggest preventive measures to maximize fish health and production efficiency by incorporating environmental sensor data, illness surveillance, and fish feeding regime data. A theoretical case study of the Salmones Multiexport (Multi X) salmon farm in the southern Chilean region of Los Lagos demonstrates how the framework can transform farm operations from a reactive response to proactive, data-driven management. Real-time monitoring, self-learning, and continuous adjustment to shifting environmental circumstances and new infections, as well as autonomous interventions and predictive modeling, are essential elements of this framework. This strategy addresses operational, ethical, and legal issues while providing a route to intelligent, resilient, and sustainable aquaculture. The study also highlights ethical, operational, and legal challenges, including infrastructure requirements, data quality, and oversight of autonomous acts.