A comparative analysis of machine learning algorithms for waste classification: inceptionv3 and chi-square features

• Published in: International journal of environmental science and technology (Tehran) Vol. 22; no. 10; pp. 9415 - 9428
• Main Authors: Yasin, E. T., Koklu, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025
• Subjects: Aquatic Pollution; artificial intelligence; automation; data collection; decision support systems; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Science and Engineering; neural networks; organic wastes; Original Paper; Soil Science & Conservation; solid wastes; waste management; Waste Water Technology; Water Management; Water Pollution Control; Waste management; InceptionV3; Chi; Classification; Machine learning; Solid waste; Feature extraction
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/s13762-024-06233-z

Effective waste management requires the correct categorization of recyclables. It is possible to classify organic waste and recyclable waste using machine learning techniques. Accurately sorting waste is important for improving recycling processes, however separating organic waste from recyclables remains a challenge. This study aimed to provide the importance of machine learning in the field of waste management and automate classification of solid waste. We compared the accuracy of three machine learning classifiers based on the Chi 2 feature selection method. Feature extraction was performed using the InceptionV3 deep convolutional neural network. The training of three machine-learning classifiers was performed using the extracted features. Based on a labeled waste classification image dataset, the performance of the classifiers was evaluated. Despite using any of the feature’s selections, SVM attained an accuracy of 96.3%, Decision Tree an accuracy of 85.8%, and KNN an accuracy of 94.9%. However, with feature selection using Chi 2 , a slight decrease in accuracy was observed. We demonstrate that machine learning algorithms can classify solid household waste with an automated model. Using the findings from this study, we can create a system that achieves optimal efficiency in terms of waste classification and management. This system can then be implemented in the real world.