Hypoxia monitoring of fish in intensive aquaculture based on underwater multi-target tracking

Abstract

Monitoring hypoxia is crucial in intensive aquaculture because changes in dissolved oxygen levels directly affect fish growth and health. Machine vision provides a cost-effective and easily calibrated alternative to traditional sensors for hypoxia monitoring. However, in practical aquaculture settings, high stocking densities and turbid water can affect monitoring accuracy. To address this, a vision-based method is proposed to monitor hypoxia by analyzing the fish behavioral changes. This method incorporates a novel tracking model, OFPTrack, and a hypoxia predictor based on a long short-term memory (LSTM) network. OFPTrack employs a tracking-by-detection strategy and enhances the precision of fish behavior data capture by leveraging underwater camera imaging principles and the three-dimensional motion characteristics of fish. Furthermore, two rematching modules are introduced to resolve short-term and long-term tracking losses by utilizing multimodal data, such as fish appearance features and the spatiotemporal characteristics of trajectories. Accuracy tests show that the MOTA of OFPTrack is competitive with models like ByteTrack and BoT-SORT, while its IDsw are significantly lower, reduced by 66.3% and 41.3%, respectively. Practical applications demonstrated that the proposed method effectively and rapidly monitors fish hypoxia. The source codes and part of the dataset are available at: https://github.com/Pixel-uu/OFPTrack.