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Real-Time Detection of Microplastics Using an AI Camera
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Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
Microplastics (MPs, size ≤ 5 mm) have emerged as a significant worldwide concern, threatening marine and freshwater ecosystems, and the lack of MP detection technologies is notable. The main goal of this research is the development of a camera sensor for the detection of MPs and measuring their size and velocity while in motion. This study introduces a novel methodology involving computer vision and artificial intelligence (AI) for the detection of MPs. Three different camera systems, including fixed-focus 2D and autofocus (2D and 3D), were implemented and compared. A YOLOv5-based object detection model was used to detect MPs in the captured image. DeepSORT was then implemented for tracking MPs through consecutive images. In real-time testing in a laboratory flume setting, the precision in MP counting was found to be 97%, and during field testing in a local river, the precision was 96%. This study provides foundational insights into utilizing AI for detecting MPs in different environmental settings, contributing to more effective efforts and strategies for managing and mitigating MP pollution.
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Researchers developed an artificial intelligence-powered holographic microscopy system that can detect and classify nanoplastics in water in real time, without any sample preparation. The technology identified particles as small as 135 nanometers and tracked their movement in three dimensions. This represents a significant advancement in environmental monitoring, as previous methods required extensive lab processing to detect plastic particles this small.
Real-time detection of microplastics in aquatic environments using emerging technologies
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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.
Automatic Detection of Microplastics in the Aqueous Environment
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Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Computer vision-based microplastic identification in water and analysis of environmental microbial mechanisms
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Scientists built an AI tool that can spot microplastics in water samples with over 97% accuracy, making it much faster than current lab methods for tracking this pollution. The tool also revealed that different types of microplastics attract different communities of bacteria, which matters because these plastic particles can act as rafts for microbes (including potentially harmful ones) as they move through waterways we depend on for drinking water and food. Faster, cheaper monitoring like this could help researchers and regulators keep closer tabs on microplastic contamination before it reaches our tap water or seafood.
Fluorescence Machine Vision-Based Rapid Quantitative Characterization of Microplastics
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Scientists developed a new system that uses special fluorescent dye and artificial intelligence to quickly detect and count microplastics (tiny plastic particles) in samples. The technology is faster and cheaper than current methods, which could help researchers better track these particles that may pose health risks when they get into our food and water. This advance could lead to better monitoring of microplastic pollution and help protect human health.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.