Next-Gen Smart Imaging System Integrating IKS for Microplastic Detection in Plant Roots.

Abstract

The increasing accumulation of microplastics in terrestrial ecosystems poses significant risks to plant physiology, soil health, and food and medicinal safety. Root systems serve as primary interfaces between plants and contaminated soils, facilitating potential microplastic uptake and bioaccumulation. This study presents the design and validation of a portable Artificial Intelligence (AI)-integrated fluorescence imaging system, termed the AI-Based Root Scanner Box, for rapid and quantitative detection of microplastics in plant root tissues. The system integrates controlled UV/blue excitation, high-resolution optical imaging, and deep learning-based segmentation algorithms to identify and quantify fluorescently stained polymer particles. Experimental validation was conducted using controlled soil treatments containing graded microplastic concentrations. The AI model demonstrated high detection accuracy, with strong agreement against Fourier Transform Infrared Spectroscopy (FTIR) validation. The proposed device offers a non-destructive, rapid, and field-deployable alternative to conventional laboratory-based microplastic detection techniques. This innovation contributes to sustainable agriculture monitoring and quality assurance of medicinal and food crops.