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61,005 resultsShowing papers similar to Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Systematic Review
ClearExploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Review
This review explores how autonomous underwater vehicles equipped with sensors could detect microplastics directly in the ocean, rather than relying on labor-intensive water sampling. Current detection methods are slow and expensive, making real-time monitoring difficult. Advances in onboard sensing technology could dramatically improve our understanding of where microplastics concentrate in marine environments.
A Review of Recent Advances in Microplastic Research and ROVs to Aid the Development of an Integrated Solution for Microplastic Pollution
This review examines recent advances in microplastic detection and filtration research alongside remotely operated vehicle (ROV) technology, with the goal of developing integrated solutions for microplastic pollution in aquatic environments. Researchers found that combining advanced detection methods with underwater robotic platforms offers a promising pathway for real-world microplastic monitoring and removal, particularly in deep or inaccessible marine and freshwater systems.
Improvement and Empirical Testing of a Novel Autonomous Microplastics-Collecting Semisubmersible
Researchers improved an autonomous microplastic-collecting robot, testing design modifications that enhanced sampling efficiency and navigation in surface water environments, moving toward practical automated monitoring of plastic pollution.
In-situ detection of microplastics in the aquatic environment: A systematic literature review
This systematic review evaluates emerging technologies for detecting microplastics directly in water environments without needing to collect samples and bring them to a lab. Developing reliable in-situ detection methods is important because current lab-based approaches are slow and expensive, making it difficult to track where microplastics are concentrated in the water systems that supply drinking water and seafood.
Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis
This review surveys marine intelligent technology—including autonomous underwater vehicles, sensor networks, and AI-based monitoring systems—as strategic tools for sustainable ocean management, including microplastic detection and marine pollution surveillance.
Use of an uncrewed surface vehicle and near infrared hyperspectral imaging for sampling and analysis of aquatic microplastics
Researchers combined an uncrewed surface vehicle with near-infrared hyperspectral imaging to sample and analyze aquatic microplastics larger than 300 micrometers. The approach demonstrated improved scalability and repeatability compared to traditional trawling methods, offering a more efficient way to monitor microplastic contamination in coastal waters.
PENGUIN
PENGUIN is a proposed autonomous underwater vehicle system designed to detect and classify plastic pollution at high spatial resolution beneath the ocean surface. Current methods for finding underwater plastics are too slow, limited, or impractical for large-scale monitoring.
Addressing Microplastic Environmental Data Gaps Through Undergraduate Research
This study proposes using underwater vehicles and standardized sampling protocols to fill data gaps on microplastic distribution in undersampled aquatic environments. The approach aims to improve spatial coverage and consistency in global microplastic monitoring datasets.
Unmanned Vehicle and Hyperspectral Imager for a More Rapid Microplastics Sampling and Analysis
Researchers tested a combination of an autonomous surface vehicle and a near-infrared hyperspectral imager to rapidly sample and identify microplastics on the Norwegian coast. Results compared favorably with standard FTIR analysis and demonstrated a repeatable method for assessing spatially variable microplastic concentrations in the marine environment.
Smart Ocean Cleanup: An AI-Integrated Autonomous System for Marine Waste Management
This paper presents an AI-powered autonomous boat system designed to detect and collect marine pollution — including plastics, oil spills, and microplastics — using deep learning image classification, IoT sensors, and robotic collection mechanisms. The system demonstrated over 94% accuracy for pollutant detection and classification across several AI models. While focused more broadly on ocean cleanup technology than on microplastic science specifically, it demonstrates how AI-integrated robotics could help address the practical challenge of removing plastic waste from ocean surfaces before it breaks down further.
Microplastic pollution in marine environments: An in-depth analysis of advanced monitoring techniques, removal technologies, and future challenges
This review provides a comprehensive analysis of microplastic pollution in marine environments, covering sources, ecological impacts, and current monitoring and removal technologies. Researchers examined physical, chemical, and biological methods for microplastic detection and cleanup, including filtration, separation, and hybrid approaches. The study concludes that while progress has been made, significant gaps remain in our ability to effectively monitor and remove microplastics from ocean ecosystems.
MantaRay: A novel autonomous sampling instrument for in situ measurements of environmental microplastic particle concentrations
Engineers developed MantaRay, an autonomous instrument that can measure microplastic particle concentrations in the ocean in real time without requiring a research ship or human operator. Automated monitoring devices like this could make large-scale, cost-effective mapping of microplastic distribution across the ocean much more feasible.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
“WAVECLEAN” – An Innovation in Autonomous Vessel Driving Using Object Tracking and Collection of Floating Debris
Researchers designed an autonomous vessel called WAVECLEAN that uses object-tracking technology to identify and collect floating marine debris, including plastics. The system combines camera-based detection with machine learning to navigate waterways and gather waste without human operation. The study demonstrates a technology-driven approach to addressing plastic pollution in harbors, rivers, and coastal areas.
State of the art detection methods of microplastics as marine litter: a mini review
This mini-review surveys the current best methods for detecting and measuring microplastics in marine environments, covering the principles, strengths, and limitations of techniques from spectroscopy to emerging real-time sensors. It highlights the ongoing challenges posed by microplastics' small size, varied composition, and widespread distribution in the ocean.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Towards Underwater Macroplastic Monitoring Using Echo Sounding
Researchers investigated using echo sounding (sonar) technology to detect and monitor underwater macroplastics in rivers and coastal environments, presenting this acoustic approach as a promising tool for measuring submerged plastic loads that surface trawling misses.
A New Approach for Detecting Oceanic Microplastics in Real Time
Researchers at Applied Ocean Sciences developed a novel real-time sensor system for detecting and analyzing oceanic microplastics, designed to overcome the time and cost limitations of conventional sampling and laboratory analysis methods. The system uses optical sensing techniques to rapidly characterize microplastics in situ, enabling more efficient large-scale ocean monitoring.
A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light
Scientists developed an AI-based method using UV light photography to automatically identify and classify different types of microplastics, achieving 86-88% accuracy. This approach is faster and cheaper than traditional lab analysis methods that require expensive equipment. Better detection tools like this are essential for understanding how widespread microplastic contamination really is in coastal environments where people live and eat seafood.
Understanding and mitigating global change with aquatic sensors: current challenges and future prospects
This paper is not about microplastics. It reviews the use of autonomous in-water sensors for environmental monitoring, discussing challenges like sensor calibration, fouling, drift, and data quality in the context of tracking global change impacts on aquatic ecosystems. While sensors could potentially be applied to monitor microplastic pollution, the paper focuses broadly on sensor technology for water quality parameters rather than on microplastics specifically.
Oceanography in the Age of Intelligent Robots and a Changing Climate
Researchers reviewed how robotic and artificial intelligence technologies are transforming ocean exploration, including monitoring of marine pollution such as microplastics. The study highlights how autonomous systems are enabling unprecedented data collection on marine chemistry, physics, geology, and biology, supporting long-term environmental monitoring efforts.
Microplastics in the Marine Environment: A Review of the Methods Used for Identification and Quantification
This review covered the methods used to identify and characterize microplastics in marine environmental samples, evaluating the strengths and limitations of visual, spectroscopic, and chemical approaches for field and laboratory analysis.
A Systematic Review of Microplastic Detection in Water
This systematic review summarizes current methods for detecting microplastics in water sources. The research highlights significant challenges in accurately measuring these tiny plastic particles, with different techniques yielding very different results. Better detection methods are essential for understanding how much microplastic is present in the water people drink and use daily.
How to Deal With Seafloor Marine Litter: An Overview of the State-of-the-Art and Future Perspectives
This review examined the state of the art for detecting and removing marine litter from the seafloor, finding that while surface and beach litter has received substantial attention, seafloor litter remains understudied and that emerging technologies including underwater robotics offer promising future cleanup pathways.