Explainable Non-Organic Waste Classification: A Comparative Study of CNN with SHAP Interpretability Against Vision Transformer Approaches
Abstract
This study develops a classification model based on Machine Learning (ML) and Computer Vision (CV) to automatically distinguish recyclable and non-recyclable waste. Waste management still faces the challenge of low manual sorting efficiency, thus reducing recycling potential. The dataset consists of 399 waste images divided with a ratio of 80:10:10 for training, validation, and testing. Three combinations of visual features were tested, namely Mean Color (RGB) + LBP + HOG, Histogram + HOG + Edge, and Mean Color (RGB) + Histogram + GLCM, each of which was evaluated using four conventional ML algorithms, namely SVM-RBF, SVM-Linear, Random Forest, and Gradient Boosting. Meanwhile, deep learning models namely CNN, ViT, and LoRA ViT were trained directly on raw images without manual feature extraction. Experimental results show that CNN achieved the highest testing accuracy of 82.50%, outperforming all conventional ML models that achieved a maximum accuracy of 75.00%, as well as ViT (72.50%) and LoRA ViT (70.00%). The application of SHAP-based Explainable AI (XAI) provides transparency to the model's decision-making process. These findings demonstrate that CNN with certain regularization settings are effective for distinguishing recyclable and non-recyclable waste, in supporting sustainable smart waste management systems.
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