Intelligent Waste Management through Automated Sorting for Enhanced Recycling and Sustainability

Year : 2024 | Volume : 15 | Issue : 03 | Page : 10 16
    By

    Shamna NV,

  • Fathima Musfira,

  • Ayshathul Shafra,

  • Abdul Baseeth A.M,

  • Mohammed Thanseer,

  1. Associate Professor, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  2. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  3. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  4. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  5. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India

Abstract

Waste segregation promotes energy production from waste, landfill depletion, recycling, and waste reduction. Recycled materials become contaminated by waste that is disposed of inappropriately. Automated computerized trash sorting is one technique to help reduce contamination, a major problem for the recycling sector. The ability to come up with models or techniques that assist individuals in sorting waste has become crucial to properly disposing of it. Even with the wide variety of recycling categories available, many people are still confused about how to select the ideal trash can for getting rid of every single waste item. Across the globe, waste management and careful sorting are considered essential elements of ecological development. Society must reduce waste by recycling and reusing discarded resources to lessen environmental concerns. Waste needs to be separated into recyclable and non-recyclable categories to be disposed of appropriately. The objective of this project is to create an automated waste detection system that gathers waste photos or videos from a camera with object recognition, detection, and prediction using a deep learning algorithm. We’ll classify the waste items, which include things like clothing, plastic, wood, paper, balls, bottles, glasses, cups, cutlery, bowls, fruit, and toothbrushes.

Keywords: Water segregation, energy, object recognition, deep learning, waste

[This article belongs to Journal of Electronic Design Technology ]

How to cite this article:
Shamna NV, Fathima Musfira, Ayshathul Shafra, Abdul Baseeth A.M, Mohammed Thanseer. Intelligent Waste Management through Automated Sorting for Enhanced Recycling and Sustainability. Journal of Electronic Design Technology. 2024; 15(03):10-16.
How to cite this URL:
Shamna NV, Fathima Musfira, Ayshathul Shafra, Abdul Baseeth A.M, Mohammed Thanseer. Intelligent Waste Management through Automated Sorting for Enhanced Recycling and Sustainability. Journal of Electronic Design Technology. 2024; 15(03):10-16. Available from: https://journals.stmjournals.com/joedt/article=2024/view=184286


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Regular Issue Subscription Original Research
Volume 15
Issue 03
Received 18/09/2024
Accepted 05/10/2024
Published 20/11/2024
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