Beyond the Bin: Machine Learning-Driven Waste Management for a Sustainable Future
DOI:
https://doi.org/10.70589/JRTCSE.2023.1.3Keywords:
waste generation, waste sorting, recycling, landfill impact, manual sorting, AI-driven solutions, Convolutional Neural NetworksAbstract
As urbanization drives an increase in waste generation, effective sorting systems become essential for enhancing recycling and reducing landfill impact. Traditional manual sorting methods are labor-intensive and error-prone, highlighting the potential of AI-driven solutions. This study addresses the urgent need for sustainable waste management by leveraging Convolutional Neural Networks (CNNs) for automated waste classification. Using a labeled dataset, we developed and trained a CNN model to distinguish between recyclable and non-recyclable waste with high accuracy, employing data augmentation and regularization techniques to enhance performance. Results demonstrate the model's effectiveness, offering a scalable solution for integrating automated waste sorting into smart cities and industrial recycling centers, contributing to a more sustainable environment.
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Copyright (c) 2023 Sanjaikanth E Vadakkethil Somanathan Pillai, Kiran Polimetla, Rajiv Avacharmal, Arun Pandiyan Perumal, Santhosh Kumar Gopal (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
