AutoSpec-PlastNet: An Integrated AI Pipeline for Automated Microplastic Detection, Size-Based Spectral Proxy Mapping, Polymer Classification, and Environmental Impact Assessment

Abstract

Microplastic pollution has recently been recognized as a significant global environmental issue because of the large-scale production, consumption, and improper disposal of plastic materials. Microplastics are generally defined as plastic particles smaller than 5 mm in size and can be divided into primary microplastics, which are intentionally produced at a microscopic scale, and secondary microplastics, which are produced by the degradation of larger plastic waste. At present, microplastics have been shown to be ubiquitously distributed in marine, freshwater, and terrestrial environments, which has severe implications for their ecological toxicity, bioaccumulation, and potential human health impacts via the food chain and water supply networks.

The primary contribution of this research effort is the design of a fully automated, start-to-finish AI system that integrates imaging, spectroscopy-based selection, polymer identification, and pollution analysis into a single framework. As the proposed system possesses the capability to realistically simulate laboratory decision-making with minimal human intervention, it is expected to offer a scalable, cost-effective, and intelligent solution for microplastic analysis.