Edge AI-Driven Multimodal Sensor Fusion for Environmental Forecasting in Smart Aquaculture Monitoring

Abstract

As the global aquaculture industry moves towards high density and integrated efficiency, the precision and immediacy of water quality management have become key to increasing productivity and reducing risks. Traditional aquaculture relies on manual experience or simple threshold controls, which often suffer from response delays and energy waste. This study proposes an IoT environmental prediction model based on edge computing designed specifically to address complex and variable outdoor aquaculture environments. The system integrates multimodal sensor data such as water level, temperature, and turbidity, and employs a 1D-CNN-LSTM (One-Dimensional Convolutional Neural Network–Long Short-Term Memory) model deployed on ESP32 edge computing nodes to achieve low-latency environmental change prediction. Based on five core control rules (turbidity control and bidirectional regulation of water level and temperature), this study simulates 360 days of operational data in a real-world environment, covering seasonal climate changes and extreme weather events (such as typhoons). Experimental results show that, compared with traditional hysteresis control, the predictive control strategy proposed in this study can provide early warnings of environmental anomalies 15 to 60 min in advance, effectively increasing the proportion of time in which water quality parameters are maintained within safe thresholds to 99.8%. This paper details the system architecture, prediction model design, and empirical benefits of long-term simulation data analysis, providing a solution with both academic depth and practical value for smart aquaculture.