An Approach to Automation in Smart Waste Classification Based on Machine Learning for Embedded Devices

This study aims to develop automated smart waste classification systems using machine learning deployed on resource-constrained hardware, such as Raspberry Pi, with the goal of integrating these models into the initial stage of waste sorting—that is, the trash bin. This study proposes two models: ConvNeXt and an improved CNN model, which is referred to as PrQ-CNN. The improved PrQ-CNN model utilizes pruning and quantization techniques to reduce its size. Both models were trained on a dataset comprising two classes: Non-recyclable and Recyclable. The data were split into a training set and a testing set with a 90:10 ratio. The models were integrated into a Raspberry Pi embedded computer to compare metrics such as model size, inference time, accuracy, missed detection rate (MDR), and false discovery rate (FDR). Experimental results show that the PrQ-CNN model achieved an accuracy of 92%, while its size was 12 times smaller than that of the original model before pruning and quantization. The ConvNeXt model achieved an accuracy of 98%. The results from the improved PrQ-CNN and ConvNeXt models were compared with VGG16, ResNet, EfficientNet, and MobileNet.