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Cross‐Disciplinary Case Studies
Summary
Researchers examined AI applications across microplastic research domains, finding that convolutional neural networks and hybrid models achieve detection and classification speeds roughly 75% faster than human analysts, and that packaging and textiles emerge as dominant identified pollution sources — pointing toward explainable, adaptive AI architectures as the next frontier.
The paper examines how Artificial Intelligence (AI) fosters microplastic research by studying different scientific fields. AI has proven itself to be a strong ally that detects microplastics while identifying different types of microplastics and their sources during a mounting environmental crisis. Three main diagrams show the quick-paced evolution of AI microplastic research together with the most prevalent algorithmic methods alongside accuracy measurements and efficiency improvements as well as the locations where pollution sources have been discovered. The performance level of Convolutional Neural Networks (CNNs) and hybrid AI models surpasses traditional detection methods by providing faster results with improved accuracy. The research demonstrates that AI systems perform analytical tasks 75% faster than human analysts while the main sources of microplastics stem from packaging along with textiles. AI brings together with environmental science to minimize workflows as well as enables extensive ecological surveillance. The study supports additional investigations on explainable adaptative AI models specifically designed for processing various environmental data. Multiplying the expertise between science disciplines creates an auspicious strategy to battle plastic pollution through system development and intelligence.