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Computer vision-based microplastic identification in water and analysis of environmental microbial mechanisms
Summary
Scientists built an AI tool that can spot microplastics in water samples with over 97% accuracy, making it much faster than current lab methods for tracking this pollution. The tool also revealed that different types of microplastics attract different communities of bacteria, which matters because these plastic particles can act as rafts for microbes (including potentially harmful ones) as they move through waterways we depend on for drinking water and food. Faster, cheaper monitoring like this could help researchers and regulators keep closer tabs on microplastic contamination before it reaches our tap water or seafood.
Microplastics have become persistent pollutants in aquatic environments due to their widespread distribution, strong adsorption capacity, and ecological toxicity. Traditional identification methods are often time-consuming and unsuitable for large-scale environmental monitoring. In this study, an explainable computer vision-based framework named MP-NetFormer was proposed for intelligent microplastic identification and environmental microbial mechanism analysis. The proposed model integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and attention mechanisms to simultaneously extract local texture features and global structural information from microscopic images. Experimental results demonstrated that the proposed framework achieved superior performance with an accuracy of 97.3%, outperforming conventional deep learning methods. In addition, microbial community analysis based on 16S rRNA sequencing revealed strong correlations between microplastic surface characteristics and microbial colonization behavior. Explainable artificial intelligence techniques further improved model interpretability by visualizing critical feature regions. The proposed framework provides an effective interdisciplinary solution for intelligent aquatic environmental monitoring and ecological risk assessment.