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Microplastic Contamination Detection in Water using Deep Learning Models
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Scientists trained a computer program (using AI image recognition) to automatically spot tiny plastic particles in water samples, and it correctly identified microplastics 99.5% of the time—faster and easier than the manual microscope checks experts currently rely on. This matters because microplastics in our water supply are linked to potential health risks, so having a quick, accurate way to detect them could help water treatment facilities catch contamination sooner and keep drinking water safer.
Microplastic contamination in water bodies has become a serious environmental concern due to its harmful impact on ecosystems and human health. Traditional microscopic detection methods are time-consuming and rely heavily on expert analysis. This study proposes an automated deep learning-based framework for detecting microplastics in microscopic water images. A custom Convolutional Neural Network (CNN) is developed and compared with transfer learning models including MobileNetV2 and ResNet50. Image preprocessing and data augmentation techniques are applied to improve feature extraction and model generalization. Experimental evaluation conducted on the Microplastic Images dataset demonstrates that MobileNetV2 achieves the highest accuracy of 99.50%, outperforming the other models. The proposed system reduces manual effort and provides an efficient solution for scalable water quality monitoring.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.