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Data driven AI (artificial intelligence) detection furnish economic pathways for microplastics

Journal of Contaminant Hydrology 2024 8 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Shefali Arora, Shefali Arora, Mamta Latwal, Mamta Latwal, Shefali Arora, Shefali Arora, K. Srirama Murthy, K. Srirama Murthy

Summary

This review examines how artificial intelligence and machine learning approaches are being applied to detect and classify microplastics in water more quickly and affordably than traditional laboratory methods. Researchers found that AI-powered image recognition and spectral analysis tools can significantly speed up identification while reducing costs. The study suggests that data-driven detection methods could make large-scale microplastic monitoring more practical and accessible.

Study Type Environmental

Microplastics pollution is killing human life, contaminating our oceans, and lasting for longer in the environment than it is used. Microplastics have contaminated the geochemistry and turned the water system into trash barrel. Its detection in water is easy in comparison to soil and air so the attention of researchers is focused on it for now. Being very small in size, microplastics can easily cross the water filtration system and end up in the ocean or lakes and become the prospective challenge to aquatic life. This review piece provides the hot research theme and current advances in the field of microplastics and their eradication through the virtual world of artificial intelligence (AI) because Microplastics have confrontation with clean water tactics.

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