Smart Dustbin and Intelligent Waste classification Using Deep Learning

Efficient waste management is a critical challenge in urban and community environments, requiring automated solutions to reduce manual labor and promote sustainability. This work introduces an integrated system combining Internet of Things (IoT) technology and Machine Learning (ML) for real-time waste detection, classification, and segregation. IoT-based sensors, such as ultrasonic modules, detect the presence of waste, while a USB camera captures images for classification. A Convolutional Neural Network (CNN) model, optimized with TensorFlow Lite, processes the images locally on an Arduino-based embedded system to classify waste as biodegradable or non-biodegradable. Actuation mechanisms, including servo motors and LED indicators, facilitate automatic sorting and provide user feedback. The system reduces manual intervention, promotes hygienic disposal, and demonstrates a cost-effective, scalable, and efficient solution for smart urban waste management.

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Publication Details

Journal
Journal of Human-Centered Design for Manufacturing
Published
2026-10-26
DOI
https://doi.org/10.65927/ah2trf92
Primary Topic
Municipal Solid Waste Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Dustbin and Intelligent Waste classification Using Deep Learning

Naveen Sundar G, Anu Ragini VP, Krishna Meena V, Jabez Gabrielraj E
Journal of Human-Centered Design for Manufacturing
Municipal Solid Waste Management
article

Smart Dustbin and Intelligent Waste classification Using Deep Learning

Naveen Sundar G, Anu Ragini VP, Krishna Meena V, Jabez Gabrielraj E
article en

Abstract

Efficient waste management is a critical challenge in urban and community environments, requiring automated solutions to reduce manual labor and promote sustainability. This work introduces an integrated system combining Internet of Things (IoT) technology and Machine Learning (ML) for real-time waste detection, classification, and segregation. IoT-based sensors, such as ultrasonic modules, detect the presence of waste, while a USB camera captures images for classification. A Convolutional Neural Network (CNN) model, optimized with TensorFlow Lite, processes the images locally on an Arduino-based embedded system to classify waste as biodegradable or non-biodegradable. Actuation mechanisms, including servo motors and LED indicators, facilitate automatic sorting and provide user feedback. The system reduces manual intervention, promotes hygienic disposal, and demonstrates a cost-effective, scalable, and efficient solution for smart urban waste management.

Journal of Human-Centered Design for ManufacturingVol. 2(3)
Karunya University (IN)
Openalex Percentile: Top 10%
Municipal Solid Waste Management
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