搜索结果：Biofouling

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.

Marine biofouling on vessel hulls poses major ecological, economic, and biosecurity risks. Traditional survey methods rely on diver inspections, which are hazardous and limited in scalability. This work investigates automated classification of biofouling severity on the Level of Fouling (LoF) scale using both custom computer vision models and large multimodal language models (LLMs). Convolutional neural networks, transformer-based segmentation, and zero-shot LLMs were evaluated on an expert-labelled dataset from the New Zealand Ministry for Primary Industries. Computer vision models showed high accuracy at extreme LoF categories but struggled with intermediate levels due to dataset imbalance and image framing. LLMs, guided by structured prompts and retrieval, achieved competitive performance without training and provided interpretable outputs. The results demonstrate complementary strengths across approaches and suggest that hybrid methods integrating segmentation coverage with LLM reasoning offer a promising pathway toward scalable and interpretable biofouling assessment.

STRings for Absorption Length in Water (STRAW)-a and b were pathfinder instruments deployed to characterize the anticipated site of the Pacific Ocean Neutrino Experiment (P-ONE), which is a future neutrino telescope that will be located in the North Pacific Ocean. Measurements of the evolution of the optical transmission efficiency from STRAW-a showed a decline over the detector's lifetime for the upward-facing modules. Video footage of the pathfinders strongly suggested this decline was caused by biofouling and sedimentation. We measure the effect of biofouling and sedimentation to be a decrease in the transparency of upward-facing optical surfaces over 5 years of operations. A majority of downward-facing optical surfaces, which will dominate P-ONE's sensitivity to astrophysical sources, showed no visible biofouling. Extrapolations motivated by biological growth models estimated that these losses started around 2.5 years after deployment, and suggest a reduction in transparency ranging from 35$\%$ of the original to complete obscuration for the upward-facing modules. Samples of biofouling were taken in order to identify the microbial diversity of these organisms and inform potenti

Biofouling$\unicode{x2013}$communities of organisms that grow on hard surfaces immersed in water$\unicode{x2013}$provides a pathway for the spread of invasive marine species and diseases. To address this risk, international vessels are increasingly being obligated to provide evidence of their biofouling management practices. Verification that these activities are effective requires underwater inspections, using divers or underwater remotely operated vehicles (ROVs), and the collection and analysis of large amounts of imagery and footage. Automated assessment using computer vision techniques can significantly streamline this process, and this work shows how this challenge can be addressed efficiently and effectively using the interpretable Component Features (ComFe) approach with a DINOv2 Vision Transformer (ViT) foundation model. ComFe is able to obtain improved performance in comparison to previous non-interpretable Convolutional Neural Network (CNN) methods, with significantly fewer weights and greater transparency$\unicode{x2013}$through identifying which regions of the image contribute to the classification, and which images in the training data lead to that conclusion. All cod

The process of fish cage inspections, which is a necessary maintenance task at any fish farm, be it small scale or industrial, is a task that has the potential to be fully automated. Replacing trained divers who perform regular inspections with autonomous marine vehicles would lower the costs of manpower and remove the risks associated with humans performing underwater inspections. Achieving such a level of autonomy implies developing an image processing algorithm that is capable of estimating the state of biofouling buildup. The aim of this work is to propose a complete solution for automating the said inspection process; from developing an autonomous control algorithm for an ROV, to automatically segmenting images of fish cages, and accurately estimating the state of biofouling. The first part is achieved by modifying a commercially available ROV with an acoustic SBL positioning system and developing a closed-loop control system. The second part is realized by implementing a proposed biofouling estimation framework, which relies on AI to perform image segmentation, and by processing images using established computer vision methods to obtain a rough estimate of the distance of the

In recent years, there has been an increasing demand for underwater cameras that monitor the condition of offshore structures and check the number of individuals in aqua culture environments with long-period observation. One of the significant issues with this observation is that biofouling sticks to the aperture and lens densely and prevents cameras from capturing clear images. This study examines an underwater camera that applies material technologies with high inherent resistance to biofouling and computer vision technologies based on image reconstruction by deep learning to lens-less cameras. For this purpose, our prototype camera uses a coded aperture with 1k rectangular shape pinholes in a thin metal plate, such as copper, which hinder the growth of biofouling and keep the surface clean. Although images taken by lens-less cameras are usually not well formed due to lack of the traditional glass-based lens, a deep learning approach using ViT (Vision Transformer) has recently demonstrated reconstructing original photo images well and our study shows that using gated MLP (Multilayer Perceptron) also yields good results. On the other hand, a certain degree of thickness for bio-rep

Biofouling represents a critical challenge in marine transportation, healthcare, and food manufacturing, among other industries, as it promotes contamination and increases maintenance costs. Zwitterionic polymers, known for their exceptional antifouling properties, offer a promising solution for biofouling deterrence. Despite the rapid development of zwitterionic polymers in recent years, the design rules, especially concerning the choice of cationic moieties to optimize biofouling deterrence, remain elusive. In this study, we leveraged a versatile all-dry synthesis scheme to achieve a selection of 9 zwitterionic polymers, 5 of which are unprecedented for this synthesis paradigm, thus systematically unraveling that molecular design rule. Notably, we developed a synthesis strategy to enable nanoscale compositional gradient along the coating cross-section, which ensures the robustness of the zwitterionic polymer coatings irrespective of the choice of cation-anion combinations. That robustness is enabled by an organosilicon-based layer at the coating-substrate interface, which simultaneously enhances coating adhesion and chemical stability while ensuring high concentration of zwitteri

Biofouling of ships causes major environmental and economic consequences all over the world. In addition, biofouling management of ship hulls causes both social, environmental and economic risks that should all be considered reaching well-balanced decisions. In addition, each case is unique and thus optimal management strategy must be considered case-specifically. We produced a novel decision support tool using Bayesian networks to promote the comprehensive understanding about the complex biofouling management issue in the Baltic Sea and to identify potential management options and their consequences. The tool compares the biofouling management strategies in relation to NIS (non-indigenous species) introduction risk, eco-toxicological risk due to biocidal coating, carbon dioxide emissions resulting from fuel consumption and costs related to fuel consumption, in-water cleaning and coating. According to the results, the optimal biofouling management strategy would consist of a biocidal-free coating with regular in-water cleaning and with devices collecting the material. However, the best biocidal-free coating type and the optimal in-water cleaning interval varies and depends e.g. on

Water monitoring is crucial for environmental monitoring, transportation, energy and telecommunication. One of the main problems in aquatic environmental monitoring is biofouling. The simplest method among the current antifouling strategies is the use of wiper technologies like brushes and wipers which apply mechanical pressure. In designing built-in strategies however, manufacturers usually build the sensor around the biofouling system. The current state-of-the-art is a fully integrated central wiper in the sensor that enables cleaning of all probes mounted on the sonde. Improvements in antifouling strategies lag rapid advancements in sensor technologies such as in miniaturization, specialization, and costs. Hence, improving built-in designs by decreasing size and complexity will decrease maintenance and overall costs. This design is targeted for the EU project Robocoenosis since bio-hybrid systems in this project incorporate living organisms. This technology targets selective proliferation of the organisms which only prevents biofilms on components where they are unwanted. Beyond this, the use of autonomous activation based on image processing may likely be advantageous for minim

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-sample subset of these images, and found that they showed 89% agreement (95% CI: 87-92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% wo