Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring
Organizations: Nanoengineered Systems Laboratory, Mechanical Engineering Department, University College London, London WC1E 7JE, UK · UCL Hawkes Institute, University College London, London W1W 7TS, UK · Department of Electrical and Computer Engineering, University of British Colombia, 5500-2332 Main Mall, Vancouver, BC Canada V6T 1Z4
Abstract
The escalating climate crisis and ecosystem degradation demand intelligent, low-cost sensors capable of robust, long-term monitoring in real-world environments. Absolute dissolved oxygen (DO) concentration is a key parameter for predicting climate tipping points. Inexpensive optoelectronic sensors based on microstructured polymer films doped with phosphorescent dyes could be readily deployable; however, signal drift and marine biofouling remain major challenges. Here, we introduce a sensing paradigm that combines camera-based DO sensors with a visual transformer (ViT)-based physics-informed neural network (PINN) for high-fidelity sensing under biofouling conditions. Training and testing data were obtained from an algae-laden water tank over 14 days to capture accelerated biofouling. The ViT-PINN, which embeds the Stern-Volmer (SV) equation into the loss function, reduces mean average error (MAE) by 92% and 89% compared to classical statistical and ML approaches, achieving ~2 umol/L absolute error. A deep ensemble further quantifies predictive uncertainty, enabling self-diagnostic sensing.