Improving Waste Classification Using Convolutional Neural Networks: An Application of Machine Learning for Effective Environmental Management

Abstract

Waste management, particularly waste sorting, constitutes a critical global challenge. The integration of advanced technology, specifically machine learning, offers potential solutions to this pressing issue. In this study, a convolutional neural network (CNN) model was employed to devise an efficient waste classification system. The model achieved notable results, attaining an accuracy rate of 98.92% and a loss percentage of only 4.03% in overall performance on the test set, utilizing the Kaggle dataset. To further improve the CNN model's performance, advanced preprocessing techniques were implemented alongside a stream lined CNN model, yielding substantial effectiveness. This investigation demonstrates that the application of machine learning techniques can result in highly accurate and efficient waste classification, presenting promising solutions for waste management challenges. By accurately identifying and sorting waste materials, this technology has the potential to significantly reduce the volume of waste directed to landfills, safeguard the environment, and conserve valuable resources.