MWaste: An app that uses deep learning to manage household waste

Suman Kunwar; Suman Kunwar

doi:10.3934/ctr.2023008

Clean Technologies and Recycling

2023, Volume 3, Issue 3: 119-133. doi: 10.3934/ctr.2023008

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Research article

MWaste: An app that uses deep learning to manage household waste

Suman Kunwar ^,

Faculty of Computer Science, Selinus University of Sciences and Literature, Ragusa, Italy

Received: 07 April 2023 Revised: 04 June 2023 Accepted: 19 June 2023 Published: 12 July 2023

Computer vision methods are effective in classifying garbage into recycling categories for waste processing but existing methods are costly, imprecise and unclear. To tackle this issue we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.
- waste classification,
- deep learning,
- household waste,
- neural networks,
- waste management,
- machine learning
Citation: Suman Kunwar. MWaste: An app that uses deep learning to manage household waste[J]. Clean Technologies and Recycling, 2023, 3(3): 119-133. doi: 10.3934/ctr.2023008

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Abstract

Computer vision methods are effective in classifying garbage into recycling categories for waste processing but existing methods are costly, imprecise and unclear. To tackle this issue we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.

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