Abstract
Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, developing an automated, innovative waste classification model becomes imperative. To address this need, our paper introduces a novel and robust solution — a smart waste classification model that leverages a hybrid deep learning model (Optimized DenseNet-121 + SVM) to categorize waste items using the TrashNet datasets. Our proposed approach uses the advanced deep learning model DenseNet-121, optimized for superior performance, to extract meaningful features from an expanded TrashNet dataset. These features are subsequently fed into a support vector machine (SVM) for precise classification. Employing data augmentation techniques further enhances classification accuracy while mitigating the risk of overfitting, especially when working with limited TrashNet data. The results of our experimental evaluation of this hybrid deep learning model are highly promising, with an impressive accuracy rate of 99.84%. This accuracy surpasses similar existing models, affirming the efficacy and potential of our approach to revolutionizing waste classification for a sustainable and cleaner future.
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All authors have contributed significantly to the conception and design. The initial manuscript was written by Shivendu Mishra, Ritika Yaduvanshi, and Prince Rajpoot from data collection, analysis, and model development. Shard Verma has contributed to the evaluation and analysis of the model result. Amit Kumar Pandey and Digvijay Pandey participated in reviewing and revising the manuscript’s content and gave their final approval for the published version. Each author has contributed sufficiently to the work to accept public responsibility for appropriate portions of the content. The final manuscript was read and approved by all authors.
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Mishra, S., Yaduvanshi, R., Rajpoot, P. et al. An integrated deep-learning model for smart waste classification. Environ Monit Assess 196, 279 (2024). https://doi.org/10.1007/s10661-024-12410-x
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DOI: https://doi.org/10.1007/s10661-024-12410-x