Dissolved oxygen in aquaculture ponds: Causal factors, predictive modeling, and intelligent monitoring

Abstract

In pond aquaculture, maintaining stable Dissolved Oxygen (DO) concentrations is essential for preventing hypoxia, optimizing growth conditions, and ensuring sustainable operations. Therefore, DO prediction is a crucial aspect of intelligent aquaculture systems, directly influencing water quality, aquatic health, and overall productivity. Advances in sensor technology and data-driven modeling have significantly enhanced the ability to monitor and forecast DO levels, enabling proactive management strategies. This review presents a novel taxonomy for classifying DO prediction approaches in pond aquaculture, structured into three key areas: (1) Driven Factors of DO, examining environmental, biological, and operational influences on DO dynamics; (2) Predictive Models, methods ranging from statistical approaches to advanced deep learning, highlighting promising techniques such as physics-informed neural networks (PINNs) and transfer learning for data-scarce environments; and (3) Monitoring and Sensor Technologies, covering electrochemical and optical sensors, particularly fluorescence-based systems, integrated with Internet of Things (IoT) platforms for real-time assessment. By synthesizing these domains, the review identifies opportunities to enhance DO prediction accuracy and monitoring reliability, supporting intelligent aeration control, improved resource efficiency, and more resilient aquaculture operations.