An Intelligent Deep Learning Approach For Accurate Recyclable Waste Categorization

Abstract

Efficient waste segregation plays a crucial role in sustainable environmental management and smart city development. Manual waste sorting is often time-consuming, inconsistent, and prone to human error. To overcome these challenges, this research proposes an intelligent deep learning-based waste classification framework capable of accurately identifying and categorizing recyclable waste items. The system employs a Convolutional Neural Network (CNN) architecture trained on image datasets containing multiple recyclable categories such as plastic, paper, glass, and metal. Advanced preprocessing techniques and data augmentation are applied to improve generalization and robustness under varying lighting and background conditions. The model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate a significant improvement in classification efficiency, making the proposed framework a reliable and scalable solution for automated waste management systems. This study highlights the potential of AI-driven technologies in promoting recycling practices and environmental sustainability.