<![CDATA[Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images, comprising 50 images for each of seven RIC categories—PETE, HDPE, PVC, LDPE, PP, PS, and OTHER—was used. Three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation attained 85.71% accuracy on the fixed 42-image test set, with perfect recall for PVC, LDPE, and PP. Most errors involved visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective solution for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.]]>
