International Journal of Science, Engineering and TechnologyAuthors: Dr Ashish Saini, Dr Amit Kumar, Dr Ankur Rana
Abstract: Rapid urbanization, population growth, and increased consumerism have led to a significant rise in municipal solid waste generation, creating serious challenges for effective waste management. Traditional waste segregation methods rely heavily on manual sorting, which is time-consuming, inconsistent, and exposes workers to hazardous materials. These limitations have motivated the exploration of automated solutions that can improve efficiency, accuracy, and safety in waste segregation processes. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated strong potential in computer vision–based waste classification tasks. CNNs are capable of automatically learningcomplex visual features such as shape, texture, and color, making them suitable for identifying different categories of waste materials. In this research, an AI-based waste sorting system was developed using a CNN model trained on publicly available waste image datasets similar to the TrashNet dataset. The system was designed to classify common waste categories, including plastic, paper, metal, glass, and organic waste. Experimental results show that the proposed model achieved an overall classification accuracy of approximately 90–92%, which is consistent with performance reported in recent literature. The findings indicate that a cost-effective and scalable AI-powered waste sorting system is feasible using existing deep learning techniques and affordable hardware components. This approach has the potential to reduce human involvement, improve recycling efficiency, and support sustainable waste management practices in urban environments.
DOI: http://doi.org/
