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Background <p>Microplastic pollution is a major environmental concern, with millions of metric tons entering the oceans yearly, threatening marine ecosystems and human health. This underscores the urgency for accessible, real-time microplastic detection technologies.</p> Objectives <p>This review examines the fusion of artificial intelligence (AI) with smartphone-based sensors, aiming to develop portable, cost-efficient microplastic detection systems, and ushering a transition from laboratory-centered to distributed environmental monitoring.</p> Methods <p>The review covers traditional microplastic detection methods such as FTIR and Raman spectroscopy and introduces emerging AI-driven techniques. It discusses advances in optical sensing (plasmonic nanowhiskers, fluorescence staining, colorimetric assays) and AI models, such as CNNs, YOLOv8, and U-Net. Smartphone hardware and computational advancements are also evaluated.</p> Results <p>AI-enhanced smartphone sensors show detection accuracies between 96% and 99.3%, outperforming conventional microscopy and approaching laboratory-level precision. These systems detect sub-micron microplastics efficiently, supported by economic analyses favoring wide deployment.</p> Conclusions <p>AI-integrated smartphone sensors are transformative tools for environmental monitoring, offering portability, accuracy, and affordability. Continued innovation and regulatory support are essential for global adoption and effective microplastic pollution management.</p>

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AI-driven portable smartphone-based sensor for microplastic detection: a comprehensive review

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Perumalla Akhil,
  • B. Nagashubha,
  • Mallam Sree Lakshmi,
  • Vallabhi Sowmya,
  • Gujjala Sonu,
  • Shaik Yashmeen Banu,
  • Chakali Veeresh,
  • Charitha Bandlapalli,
  • Maccha Kiran Sai

摘要

Background

Microplastic pollution is a major environmental concern, with millions of metric tons entering the oceans yearly, threatening marine ecosystems and human health. This underscores the urgency for accessible, real-time microplastic detection technologies.

Objectives

This review examines the fusion of artificial intelligence (AI) with smartphone-based sensors, aiming to develop portable, cost-efficient microplastic detection systems, and ushering a transition from laboratory-centered to distributed environmental monitoring.

Methods

The review covers traditional microplastic detection methods such as FTIR and Raman spectroscopy and introduces emerging AI-driven techniques. It discusses advances in optical sensing (plasmonic nanowhiskers, fluorescence staining, colorimetric assays) and AI models, such as CNNs, YOLOv8, and U-Net. Smartphone hardware and computational advancements are also evaluated.

Results

AI-enhanced smartphone sensors show detection accuracies between 96% and 99.3%, outperforming conventional microscopy and approaching laboratory-level precision. These systems detect sub-micron microplastics efficiently, supported by economic analyses favoring wide deployment.

Conclusions

AI-integrated smartphone sensors are transformative tools for environmental monitoring, offering portability, accuracy, and affordability. Continued innovation and regulatory support are essential for global adoption and effective microplastic pollution management.