Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring

• URL: http://arxiv.org/abs/2604.24236v1
• Date: Mon, 27 Apr 2026 09:44:56 GMT
• Title: Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring
• Abstract summary: 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.<n>The ViT-PINN embeds the Stern-Volmer equation into the loss function, reducing mean average error (MAE) by 92% and 89% compared to classical statistical and ML approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Robust Extended Kalman Filter for Land Navigation Using Massive Array of MEMS IMUs [2.8647133890966994]
  We propose a robust Extended Kalman Filter (EKF) architecture for land navigation using an array of low-cost micro-electromechanical systems (MEMS) inertial sensors.<n>The main challenges in this setting are bursty sensor-specific bias errors, bias drift, and the need to aggregate many inertial measurements.<n>We introduce Robust Inertial Sensor Array Fusion (RISAF), a pre-filtering framework that combines dynamic percentile gating with real-time bias tracking.
  arXiv  Detail & Related papers  (2026-06-28T08:37:33Z)
• FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture [8.150520348578087]
  FinSight-Net is an efficient and physics-aware fish detection framework for aquaculture environments.<n>We show that FinSight-Net reaches 92.8% mAP, outperforming YOLOv11s by 4.8% while reducing parameters by 29.0%.<n>In particular, on UW-BlurredFish, FinSight-Net reaches 92.8% mAP, outperforming YOLOv11s by 4.8% while reducing parameters by 29.0%.
  arXiv  Detail & Related papers  (2026-02-23T02:12:47Z)
• Active Optics for Hyperspectral Imaging of Reflective Agricultural Leaf Sensors [0.0]
  Monitoring plant health increasingly relies on leaf-mounted sensors that provide real-time physiological data.<n>We present an integrated, adaptive, and scalable system that autonomously detects and interrogates plant sensors.
  arXiv  Detail & Related papers  (2025-12-11T02:03:07Z)
• Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy [0.5002873541686897]
  pesticides and synthetic dyes pose critical threats to food safety, human health, and environmental sustainability.<n>Raman spectroscopy offers molecularly specific fingerprints but suffers from spectral noise, fluorescence background, and band overlap.<n>Here, we propose a deep learning framework based on ResNet-18 feature extraction to detect pesticides and dyes from Raman spectroscopy, called MLRaman.
  arXiv  Detail & Related papers  (2025-11-15T11:35:55Z)
• Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels [2.729898906885749]
  We present a bio-hybrid environmental sensor system that integrates natural plants and embedded deep learning.<n>Our system records electric differential potential signals from Hedera helix and processes them onboard using an embedded deep learning model.<n>We demonstrate that our sensing device detects changes in temperature and ozone with good sensitivity of up to 0.98.
  arXiv  Detail & Related papers  (2025-09-29T16:19:31Z)
• Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA's EMIT and PACE Missions [17.569943344077892]
  This study presents novel machine learning-based solutions for NASA's hyperspectral missions, including EMIT and PACE.<n>We first time tailor the VAE model with innovative designs to achieve hyperspectral retrievals of aphy and of Chl-a from hyperspectral Rrs in optically complex estuarine-coastal waters.
  arXiv  Detail & Related papers  (2025-04-18T05:37:14Z)
• Optical Quantum Sensing for Agnostic Environments via Deep Learning [59.088205627308]
  We introduce an innovative Deep Learning-based Quantum Sensing scheme. It enables optical quantum sensors to attain Heisenberg limit (HL) in agnostic environments. Our findings offer a new lens through which to accelerate optical quantum sensing tasks.
  arXiv  Detail & Related papers  (2023-11-13T09:46:05Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.