Advancing microplastic detection in water systems using SEM imaging and AI analysis

The presence of microplastics in aquatic environments is a growing environmental concern, threatening both ecosystem health and human safety. Traditional detection methods for microplastics are often hampered by poor precision and lack of scalability. Existing techniques for identifying microplastics in water are inadequate due to their limited accuracy, efficiency, and scalability. Given the widespread nature of microplastic pollution, there is a pressing need for more effective detection methods that can operate across various water bodies. We propose a novel method for microplastic detection that combines Scanning Electron Microscopy (SEM) with Artificial Intelligence (AI) technologies. This approach aims to significantly improve the precision, efficiency, and scalability of microplastic identification processes. Our process includes collecting water samples, which are then prepared for SEM to obtain highresolution images of microplastics. These images undergo preprocessing to enhance clarity and reduce noise, followed by manual annotation to create a training dataset for an AI model. The model, which employs advanced convolutional neural networks (CNNs), is then trained, and validated for accuracy in detecting microplastics. The developed methodology offers a reliable solution for the accurate and scalable detection of microplastics in water systems. This advancement will aid in better understanding the distribution and impact of microplastic pollution, ultimately supporting the development of informed policies and management strategies to address this global environment.