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Papers
61,005 resultsShowing papers similar to Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis
ClearDigital Oceans: Artificial Intelligence, IoT, and Sensor Technologies for Marine Monitoring and Climate Resilience
This book-length review examines how artificial intelligence, Internet of Things sensors, and advanced marine technologies are being used to monitor ocean health in real time, including tracking pollutants like microplastics. The authors survey emerging tools for marine environmental monitoring, from autonomous underwater vehicles to satellite-based detection systems. The work highlights how digital technologies could transform our ability to detect and respond to ocean pollution threats.
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.
Managing Marine Environmental Pollution using Artificial Intelligence
This review explores how artificial intelligence technologies are being developed to monitor and manage marine environmental pollution, including plastic contamination. The study suggests that AI-based approaches such as automated detection and predictive modeling offer promising opportunities for understanding ocean pollution and supporting sustainability goals.
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.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Systematic Review
This systematic review explores how autonomous underwater vehicles (AUVs) could be used to detect microplastics in the ocean in real time, replacing slower traditional sampling methods. While promising, the technology is still developing and faces challenges with sensor accuracy and deep-water operation. Better detection tools like these could help scientists understand how widespread microplastic contamination really is in marine environments.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation
This review examines the growing application of artificial intelligence and machine learning in maritime and marine environment management. The study covers how AI technologies are being used to improve sustainability, efficiency, and regulatory compliance in the marine industry, including environmental monitoring relevant to pollution tracking.
Exploring 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.
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.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
AI for Monitoring Ocean Plastic Pollution
This review assessed how artificial intelligence technologies—including satellite image analysis, computer vision, and machine learning—are being applied to monitor ocean plastic pollution. The authors found AI can dramatically expand spatial coverage and detection speed compared to traditional ship-based surveys, though ground-truth validation and data standardization remain challenges.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
The Development of Sensors for Microplastic Detection Using Artificial Intelligence
This review examined AI-enhanced sensors developed for microplastic detection and characterization in aquatic environments, covering machine learning, deep learning, and spectroscopic sensor approaches. The authors found that AI substantially reduces the labor intensity of microplastic identification and improves detection of small particles, though training dataset standardization and real-world validation remain priority challenges.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
Advancing microplastic pollution management in aquatic environments through artificial intelligence
This review examines how artificial intelligence and robotics are being applied to tackle microplastic pollution in aquatic environments, covering waste collection, particle identification, and degradation monitoring. Researchers highlight several successful AI-driven projects deployed by countries and organizations around the world. The study suggests that integrating AI with traditional environmental methods holds significant promise for improving both the speed and accuracy of microplastic management.
Utilizing Artificial Intelligence (AI) for the Identification and Management of Marine Protected Areas (MPAs): A Review
This review examined how artificial intelligence and automation can improve the identification and management of marine protected areas, including advances in data gathering, monitoring, and analysis for more effective marine conservation.
Artificial Intelligence Technologies in the Monitoring and Analysis of Water Resources Data (An Analytical Study)
This review examines the application of artificial intelligence technologies — including smart sensors, satellites, and unmanned aerial vehicles — to the monitoring and analysis of water resources data. Researchers found that AI-powered platforms significantly improve data collection efficiency and analytical capacity for managing water quality and quantity, including emerging contaminants such as microplastics.
Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
Role of AI Technique for Controlling Micro Plastic on Marine Eco System
This paper developed a machine learning system using Support Vector Machine (SVM) algorithms to classify microplastic density in ocean water based on oceanographic sensor data, achieving 93% accuracy. The system is proposed as a scalable, automated alternative to labor-intensive manual microplastic sampling in marine environments. AI-driven monitoring tools like this could make it practical to track plastic pollution across vast ocean areas where manual surveys are infeasible.
A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution
This review examines how remote sensing technology, including satellites and drones, is being used to monitor marine pollution such as oil spills, floating debris, and microplastics. While the technology works well for detecting large-scale pollution, methods for tracking microplastics in the ocean are still in early development. Better monitoring tools are needed to understand the full scope of marine microplastic pollution, which ultimately affects seafood safety and human health.
Aquatic Trash Detection and Classification: a Machine Learning and Deep Learning Perspective
This review examines machine learning and deep learning approaches for detecting and classifying aquatic trash in waterways, evaluating how computer vision algorithms trained on underwater and surface imagery can automate pollution monitoring for faster, more scalable ocean cleanup.
FindingPlastic: Underwater Plastic Bag Detection and Retrieval
Engineers developed an automated system using artificial intelligence to detect, track, and capture floating plastic bags underwater before they break down into microplastics. The system combines modern object detection and tracking algorithms and was successfully tested in a tank environment, offering a potential tool for robotic ocean cleanup efforts.
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.
Real-time detection of microplastics in aquatic environments using emerging technologies
Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.