Optimizing Contaminant Detection in Curbside Recycling Streams with IoT and Deep Learning

Improving recycling efficiency and decreasing contamination requires effective management of curbside recycling streams. In this research, we provide an advanced approach for improving the identification of contaminants in curbside recycling streams by integrating Internet of Things (IoT) technologies with Deep Learning, especially the VGG16 convolutional neural network (CNN). Using the IoT and sensors, we propose a system that can take high-resolution images of recyclables and process them using the VGG16 model. Accurately identifying and classifying pollutants inside recycling streams is the training ground for this deep learning (DL) model, renowned for its powerful image classification skills. Using VGG16, various pollutants may be accurately detected and classified, leading to better recycling quality and reduced operation expenses. The proposed system substantially increases operational efficiency and detection accuracy, providing a scalable solution and allowing municipalities and recycling facilities to improve their recycling operations. The findings highlight the efficacy of integrating IoT with advanced DL methods for recycling stream management and pollution mitigation. The proposed CNN model achieved an accuracy of 94%, decreasing contamination by 35% in IoT-enabled curbside recycling and improving sorting efficiency by 40%.