Smart Waste Management and Classification Systems Using Cutting Edge Approach

Sehrish Munawar Cheema, Abdul Hannan () and Ivan Miguel Pires ()
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Sehrish Munawar Cheema: Department of Computer Science, University of Management and Technology, Sialkot 51310, Pakistan
Abdul Hannan: Department of Computer Science, University of Management and Technology, Sialkot 51310, Pakistan
Ivan Miguel Pires: Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal

Sustainability, 2022, vol. 14, issue 16, 1-21

Abstract: With a rapid increase in population, many problems arise in relation to waste dumps. These emits hazardous gases, which have negative effects on human health. The main issue is the domestic solid waste collection, management, and classification. According to studies, in America, nearly 75% of waste can be recycled, but there is a lack of a proper real-time waste-segregating mechanism, due to which only 30% of waste is being recycled at present. To maintain a clean and green environment, we need a smart waste management and classification system. To tackle the above-highlighted issue, we propose a real-time smart waste management and classification mechanism using a cutting-edge approach (SWMACM-CA). It uses the Internet of Things (IoT), deep learning (DL), and cutting-edge techniques to classify and segregate waste items in a dump area. Moreover, we propose a waste grid segmentation mechanism, which maps the pile at the waste yard into grid-like segments. A camera captures the waste yard image and sends it to an edge node to create a waste grid. The grid cell image segments act as a test image for trained deep learning, which can make a particular waste item prediction. The deep-learning algorithm used for this specific project is Visual Geometry Group with 16 layers (VGG16). The model is trained on a cloud server deployed at the edge node to minimize overall latency. By adopting hybrid and decentralized computing models, we can reduce the delay factor and efficiently use computational resources. The overall accuracy of the trained algorithm is over 90%, which is quite effective. Therefore, our proposed (SWMACM-CA) system provides more accurate results than existing state-of-the-art solutions, which is the core objective of this work.

Keywords: waste classification; waste management; image recognition; smart city; smart environments; convolutional neural network (CNN); internet of things (IoT); deep learning (DL); sustainability and environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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