An IoT-Enabled Intelligent Waste Segregation Framework using Deep Learning & Multi - Sensor Fusion for Automated Smart Waste Management
DOI:
https://doi.org/10.15662/IJEETR.2026.0802385Keywords:
Internet of Things (IoT), Deep Learning, Convolutional Neural Network (CNN), Sensor Fusion, Waste Segregation, Embedded Systems, Smart Waste Management, Edge Computing, Image Classification, Automation, Intelligent Systems, Smart Cities, Cloud Monitoring.Abstract
Rapid urbanization and changing consumption patterns have led to a significant increase in municipal solid waste, posing serious environmental and public health challenges. Inefficient manual waste segregation methods often result in improper recycling, increased landfill usage, and higher operational costs. To address these issues, this project proposes an IoT-Based Intelligent Waste Segregation System using Deep Learning and Sensor Fusion, designed to automatically classify and segregate waste in real time.
The proposed system integrates multiple sensors—such as metal detectors, load cells, and proximity sensors—with a camera-based vision module to capture both physical and visual characteristics of waste objects. A deep learning–based image classification model analysis the captured images to identify waste categories such as organic, plastic, and metal. Sensor fusion techniques combine data from heterogeneous sensors to improve classification accuracy and reliability.
The segregated waste is directed into appropriate compartments using automated actuation mechanisms, while operational data is transmitted to a cloud platform through IoT communication protocols for monitoring and analysis. After identifying the waste type, a servo-based actuation mechanism directs the item to the appropriate disposal container.
Operational data, such as bin fill levels and item counts, are continuously transmitted to a cloud platform through an IoT gateway for real-time monitoring. Experimental evaluation of the prototype demonstrates enhanced sorting accuracy and a significant reduction in manual intervention.
Overall, the proposed AI- and IoT-driven architecture offers a scalable, cost-efficient, and practical solution for automated waste management in smart-city infrastructures. The system minimizes human intervention, enhances segregation efficiency, and reduces contamination between waste categories. By enabling accurate waste sorting at the source, the proposed solution supports sustainable waste management practices, improves recycling effectiveness, and contributes to smart city and environmental sustainability initiatives.
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