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Papers
20 resultsShowing papers similar to Digital Oceans: Artificial Intelligence, IoT, and Sensor Technologies for Marine Monitoring and Climate Resilience
ClearOceanography 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.
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.
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.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
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.
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.
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.
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.
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.
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.
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.
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.
Detecting Chemical Contaminants in Water Using AI
This review examines how artificial intelligence and machine learning tools are being applied to detect chemical contaminants in water, including microplastics, covering sensor technologies, data processing approaches, and the potential for real-time monitoring systems.
Artificial Intelligence (AI) to Trace the Pathways of MPs
This book chapter examines how artificial intelligence tools—including machine learning and remote sensing—can be used to trace microplastic transport pathways across environments, improving the accuracy and scale of MP distribution mapping beyond what conventional monitoring can achieve.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
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.
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring
This review examines advances in sensor technology, autonomous deployment methods, and artificial intelligence for monitoring water quality in real time across rivers, lakes, and oceans. Researchers describe how networks of sensors on robotic boats, buoys, and drones can now measure physical, chemical, and biological water parameters more broadly than ever before. The study proposes that connecting water monitoring systems globally could help address challenges related to drinking water safety, aquaculture, and emerging contaminants like microplastics.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
Survey on IoT Based Microplastic Detection
This research review summarizes new technology that uses internet-connected sensors to detect tiny plastic particles (microplastics) in water in real-time, rather than relying on slow lab tests. Microplastics are a growing health concern because they can get into our drinking water and food chain, potentially harming human health. Better detection methods could help protect our water supplies by catching pollution problems faster.