Automated waste classification using computer vision has become essential for improving environmental sustainability and reducing manual sorting effort. This study presents an enhanced waste image classification model based on EfficientNet-B0, trained using a two-stage transfer learning strategy that combines feature extraction and fine-tuning. The proposed approach aims to enhance classification accuracy while maintaining computational efficiency. Experimental evaluations conducted on a heterogeneous multi-class waste dataset demonstrate the superiority of the proposed method. The confusion matrix results indicate a high proportion of correct predictions across most categories, with only minor misclassifications among visually similar classes, such as metal and paper. The model's robustness is further validated through 5-Fold Cross-Validation, which yields an average accuracy of 94.3% with a standard deviation of ±0.007, confirming consistent performance across data partitions. Compared with state-of-the-art CNN architectures, including ResNet50 and DenseNet121, the proposed model achieves the highest accuracy while using the fewest parameters (4.38M), making it suitable for deployment in resource-constrained environments. Additionally, qualitative analysis using Grad-CAM confirms that the model’s decisions are explainable and based on relevant object features. These findings demonstrate that the proposed EfficientNet-B0 model constitutes a reliable, efficient, and interpretable solution for automated waste classification. The model is further evaluated using cross-validation and explainable AI (Grad-CAM) to assess both performance stability and interpretability.
Manik et al. (Thu,) studied this question.