A Deep Learning Approach to Smart Waste Classification for Sustainable Environments

A key element of sustainable development is efficient trash classification, which aims to minimize environmental damage and expedite recycling procedures. In addition to being time-consuming, traditional human sorting methods are prone to mistakes, which makes waste management systems less effective. Automated garbage classification has attracted so much attention as AI, especially ML and DL, has grown. However, because they frequently rely on small-scale datasets and traditional architectures, many of the models that are now in use have issues with generalization, poor performance, and high error rates. This work presents a hybrid deep learning system that combines an autoencoder with a vision transformer (ViT) to address these issues. By efficiently capturing local and global data, our design improves classification robustness and accuracy across various waste types. Our model was trained and assessed using a sizable and varied dataset to enhance generalization to real-world scenarios. According to experimental data, the suggested model achieves a precision of 96.72%, a recall of 96.21%, an F1-score of 96.46%, and a balanced accuracy of 96.48%, outperforming some cutting-edge CNN-based architectures. Furthermore, sophisticated measures like Cohen's Kappa (95.90%) and Matthews Correlation Coefficient (MCC = 94.91%) confirm the dependability of our solution. Lastly, by successfully implementing the model in an inference pipeline, we show that it is ready for real-world deployment and that it has the potential to promote sustainable development goals through scalable, intelligent waste management.