Automated Detection And Classification Of Recyclable and Non-Recyclable Waste Using AI And CNN

Waste management has become a major global challenge due to the rapid increase in population and consumption. Improper waste disposal leads to environmental pollution and health risks. Effective waste segregation is essential for recycling and sustainable waste management. However, manual sorting of waste is time-consuming and prone to errors. This creates a need for automated systems that can accurately classify different types of waste materials.in this paper, a waste classification system based on Convolutional Neural Networks (CNN) is proposed to automatically identify waste categories such as plastic, metal, paper, glass, cardboard, and organic waste. The system uses image data as input and preprocessing techniques including applies resizing, a augmentation to nd normalization, improve model performance. The CNN model is trained to learn visual patterns such as texture, shape, and colour from the images, enabling it to accurately classify different types of waste. The experimental results demonstrate that the proposed model achieves high classification accuracy and performs well on unseen data. The system shows strong generalization capability and effectively distinguishes between different waste categories. The deployment of the model using a web-based interface further enhances its practical usability. The proposed approach highlights the potential of deep learning techniques in improving automated waste management systems and promoting environmental sustainability.