Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

Abstract

This paper presents a hybrid model that is used to measure the waste recyclability level using a convolutional neural network (CNN) and fuzzy inference system (FIS; WRL-CNNFIS). The proposed system uses waste images to train a multilayer convolutional neural network to extract the most relevant features that were used in a rule-based fuzzy system to give an accurate percentage of the recyclability level of these images. The proposed model did overcome many challenges in transfer learning models alone, like overfitting and low accuracy. The use of fuzzy rules improved the performance even with a small data set. Results have shown the effectiveness of the proposed model in terms of all four metrics: accuracy, precision, recall, and F1 score. The performance was measured under two testing scenarios. For all evaluation measurements in all experiments, the validation was conducted using the cross validation in the last step. The proposed approach is a robust and consistent approach for classifying organic and recyclable waste types. WRL-CNNFIS has achieved an accuracy rate of more than 98%.