Cost-effective method for degradability identification of MSW using convolutional neural network for on-site composting

Abstract

Automatically identifying the degradability of municipal solid waste (MSW) is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes. In this study, a cost-effective method was proposed for the degradability identification of MSW. Firstly, the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site. Secondly, a lite convolutional neural network (CNN) model was built with only 3.37 million parameters, and then a total of eight models were trained on these datasets with and without the image augmentation operations, respectively. Finally, a degradability identification system was built for on-site composting, where the images were cut to different sizes of small squares for prediction, and the experiments were conducted to find the best combinations of the trained models and the cutting size. The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher, and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%, which indicated the capability of this cost-effective method to identify the degradability of MSW.