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Article . 2025
License: CC BY NC
Data sources: ZENODO
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Article . 2025
License: CC BY NC
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY NC
Data sources: Datacite
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AI-Driven Waste Classification and IOT-Based Bin Monitoring with AR Visualization Support: A Review

Authors: Vedant Mansukh Rathod; Chirag Kamlesh Agarwal; Dr. Vandana .V. Kale;

AI-Driven Waste Classification and IOT-Based Bin Monitoring with AR Visualization Support: A Review

Abstract

To keep pace with the growing quantities of garbage accumulating in towns is a challenge that has intruded into the economy to become of real concern for public health and environmental sustainability. Poor waste collection is still synonymous with delays in having waste picked up, improper marking off, and poor environmental maintenance. It tries to build a Smart Waste Management System that replaces the traditional waste collection system by signifying the use of Augmented Reality (AR), Internet of Things (IoT), and Artificial Intelligence (AI) in various aspects such as improved segregation, enhanced recycling, and optimized collection. AI-image classification algorithms will detect waste from the camera feed and categorize it into six primary classes: plastic, metal, paper, glass, biodegradable, and cardboard-thus eliminating the entire category by too long a grade classification. IoT provides for a smart bin having sensors to monitor fill levels and waste types and send that data to a server for further processing. AR interface overlays existing information on the user, giving instant rewards for waste disposal into the right compartments and public engagement and awareness. All savings in fuel, optimizations of collection routes, and increased recycling will be from analytics derived from the usage data. Indeed, it has an above-average accuracy of 85% for each differentiated piece of waste as per learning on different datasets by the AI model. The prototypes

Keywords

IoT, Sustainability, AI, Real-Time Monitoring, Smart Waste Management, Waste Segregation, AR

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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