Deep Learning based Waste Material Classification

Efficient waste management practices are crucial for mitigating environmental risks, preserving human health, and promoting overall well-being. As waste generation continues to escalate globally, traditional waste disposal methods face challenges in coping with the increasing volume and complexity of waste materials. In response, novel approaches leveraging advancements in technology and artificial intelligence have emerged to revolutionize waste management processes. This paper presents comprehensive research on the application of deep learning techniques, specifically ensemble methods combined with transfer learning, for automated waste classification. The proposed approach aims to streamline waste segregation, reduce human intervention, and enhance the accuracy and efficiency of waste categorization. By integrating pre-trained VGG16 and ResNet50 models, and utilizing a hardware setup with a servo arm, the system enables automated waste material identification and sorting. Experimental findings show that the suggested solution is successful in waste management scenarios, contributing to a cleaner and more sustainable environment.