DEEP LEARNING FOR WASTE CLASSIFICATION
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The solid waste generated has been increasing rapidly due to the growth in population and consumption. This is a major issue, particularly in cities. Waste segregation is currently mostly done by hand, which is time consuming and energy demanding, and can be inaccurate when various kinds of waste are mixed. This calls for more efficient techniques. The use of machine learning and deep learning is making the classification of waste more efficient. These methods can assist in automatically classifying different waste items from images. But most of these systems have been developed for simple problems, such as classifying only two types of waste, and also work well only for a good image. This makes them less suitable to use in real scenarios when waste is not well categorised or images are not ideal. In this study, various machine learning and deep learning techniques are discussed, particularly convolutional neural networks and networks such as VGG. Their effectiveness is evaluated in terms of their classification accuracy for various types of waste and under different circumstances. The research also identifies some issues and suggests how they can be addressed. The overall objective is to learn how to make these technologies more applicable to waste management.
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Kunjwal, A. (2026). Deep Learning for Waste Classification. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.197
Kunjwal, Anjali. "Deep Learning for Waste Classification." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.197.
Kunjwal, Anjali. "Deep Learning for Waste Classification." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.197.
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