Design of an Intelligent Agent for Offshore Cage Aquaculture Based on a Cold-Damage Early-Warning Model

Abstract

Climate-change-driven anomalies in seawater temperature frequently trigger cold-damage events, leading to significant fish mortality and economic losses in marine cage aquaculture. Although Internet of Things (IoT) systems and marine meteorological observation (MMO) data have been widely used for environmental monitoring, challenges remain in addressing microclimate data scarcity, domain shift, and the prediction of rare extreme events, limiting their effectiveness for actionable farm management. In this study, long-term (2000–2025) MMO datasets from 14 stations of the Taiwan Central Weather Administration, including seawater temperature, air pressure, and wave dynamics, were integrated with real-time offshore sensor data. Based on these inputs, a time-series deep-learning-based intelligent agent, TPP-CASTformer, is proposed. To address data scarcity and domain shift, Test-Time Training (TTT) enables model adaptation during inference using unlabeled target-site data. In addition, a classification mechanism combining Temporal Point Processes and Prototypical Networks is designed to improve interpretability and handle extreme-event imbalance through few-shot learning. The proposed framework integrates heterogeneous MMO and local sensing data, incorporates domain knowledge to define temperature thresholds and exposure durations, and provides interpretable early-warning signals with corresponding management actions. Experimental results based on documented cold-damage events in Penghu, Taiwan indicate that the proposed approach can reduce potential losses by at least 30%, demonstrating improved accuracy and timeliness in cold-damage risk assessment.

Keywords: Intelligent agent, Offshore cage aquaculture, Marine meteorological observation data, Internet of Things, Time-series deep learning model, Cold-damage early-warning model.

DOI: 10.54941/ahfe1007277