Automated Waste Classification for Sustainable Cities Using YOLO Based CNN Integrated IoT

Sustainable waste management is a vital component of smart city development, directly impacting environmental quality and recycling efficiency. This study presents an IoT-enabled waste classification system that utilizes a Convolutional Neural Network (CNN) for accurate, real-time identification of organic and non-organic waste. The model, implemented using the YOLO architecture, was trained on a diverse dataset of waste images captured under various environmental conditions to ensure robustness in practical scenarios. Classification results are automatically stored in a MySQL database and visualized via an Internet of Things (IoT) based Node-RED dashboard, enabling municipal operators to monitor waste categories and quantities remotely. Field evaluations demonstrate that the system achieves an accuracy of 94%, precision of 94.5%, recall of 93.2%, and an F1-score of 93.85%, indicating high detection reliability and consistent performance, even in challenging urban environments. By integrating CNN-based deep learning with IoT visualization tools, this approach offers a scalable and efficient solution that supports sustainable waste management initiatives within smart city frameworks.