A Web-Based Intelligent System for Automated Detection, Classification, and Analysis of Microplastics from Microscopic Images Using Ultralytics YOLO

Abstract: Microplastics (MPs (Micro, (Microplastics), 1 µm-5 mm) and nano plastics (NPs,(Nanoplastics), <1 µm) pose significant environmental and health risks, yet traditional detection methods are slow and resource-intensive. MPWebAI is a browser-based platform enabling users to upload microscopic images for automated MP detection, morphological classification (fibre, film, fragment, pellet), and quantitative analysis using Ultralytics YOLOv11m. Trained on a 7,200-image curated dataset, the model achieves mAP@50 of 95.4%, precision 94.2%, and recall 92.8%. The Fast API + React web app provides annotated images, particle counts, size statistics, and reports in seconds, requiring no specialized hardware beyond a standard microscope and smartphone. A review of current NP detection methods-including optical sieves, SERS, nano-DIHM, and AI-enhanced spectroscopy-highlights challenges for sub-micron particles such as matrix interference, spectral overlap, and the absence of certified reference materials. The paper outlines extensions integrating higher-resolution imaging and hybrid AI-spectroscopy. MPWebAI democratizes monitoring for researchers and citizen scientists in resource-limited regions. The system is open-source.

Keywords: Microplastics, Nano plastics, Ultralytics YOLOv11, Object Detection, Web-Based AI, Environmental Monitoring, Deep Learning.