STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Electronic Design Technology Original Research

Intelligent Waste Management through Automated Sorting for Enhanced Recycling and Sustainability

  1. Shamna NV Department of Computer Science and Engineering, P A College of Engineering
  2. Fathima Musfira Department of Computer Science and Engineering, P A College of Engineering
  3. Ayshathul Shafra Department of Computer Science and Engineering, P A College of Engineering
  4. Abdul Baseeth A.M Department of Computer Science and Engineering, P A College of Engineering
  5. Mohammed Thanseer Department of Computer Science and Engineering, P A College of Engineering

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

References (11)

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