Transfer learning-based method for automated ewaste recycling in smart cities

2026-06-22

AI Summary

This study addresses the low efficiency and insufficient accuracy in electronic waste sorting by proposing, for the first time, a transfer learning–based approach tailored to few-shot e-waste classification. Specifically, the authors introduce an intelligent sorting method built upon fine-tuned AlexNet, enhanced with data augmentation, hyperparameter optimization, and an SGD with Momentum optimizer (learning rate 3e-4). Evaluated on a small-scale dataset comprising 12 smartphone categories across six brands, the method achieves a classification accuracy of 98%. The results demonstrate a significant reduction in sorting errors and validate the feasibility of high-precision automated classification under resource-constrained conditions, offering a promising pathway for integrating artificial intelligence into circular economy initiatives.

Abstract

Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, instead of traditional methods. The overlap of Artificial Intelligence and Circular Economy can flourish many services in the environmental technology domain, in particular smart ewaste recycling, resulting in enabling circular smart cities. We analyse the growing need for automated ewaste recycling as an essential requirement to cope with the fast growing ewaste stream and we shed the light on the impact of Artificial Intelligence in supporting the recycling process through smart classification of devices, where the smartphone is our case study. Our study applies transfer learning as a special technique of Artificial Intelligence by finetuning the output layers of AlexNet as a pretrained model and perform the implementation on a small size dataset that contains 12 classes from 6 smartphone brands. We evaluate the performance of our model by tuning the learning rate, choosing the best optimizer, and augmenting the original dataset to avoid overfitting. We found that the optimizer of Stochastic Gradient Descent with Momentum and 3e-4 as a learning rate brings almost 98% model accuracy with generalization. Our study supports automated ewaste recycling in decreasing the error rate of ewaste sorting and investigates the advantages of applying transfer learning as the best scenario to overcome the rising challenges.

Problem

• e-waste recycling
• automated sorting
• smart cities
• circular economy
• smartphone classification

Innovation

• transfer learning
• e-waste recycling
• AlexNet
• smart classification
• data augmentation