MSW-Net: A hierarchical stacking model for automated municipal solid waste classification

Efficient solid waste management is crucial for urban health and welfare in the midst of fast industrialization and urbanization. In this changing environment, government authorities have a significant role in addressing and reducing the effects of solid waste. While waste separation at the source simplifies processes, manual sorting is a consequence of ignorance in numerous regions, which endangers the health of waste pickers. This study addresses the challenges by introducing the MSW-Net model, a hierarchical stacking model designed for the automated classification of municipal solid waste (MSW). Customized Convolutional Neural Network (custom CNN) and Bayesian-Optimized MobileNet models serve as the base models, with Gradient Boosting employed as the meta-classifier. The MSW-Net model, as proposed, exhibits exceptional performance, attaining 99%, 95%, and 96.43% accuracy rates over training, validation, and testing, respectively. Additionally, the model achieves precision, recall, and F1 scores of 96.42%, 96.43%, and 96.42% during the testing phase. Therefore, the proposed MSW-Net model performs better than the other existing models in sorting the waste. This could also aid the municipal authorities in classifying the waste with minimal human intervention.