EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities

Waste management is a critical environmental and economic issue worldwide. Existing waste segregation ac- tivities are inefficient, resulting in high landfill contributions and environmental contamination. In this paper, an artificial intelligence-based waste categorization and management system, EcoCycle, is proposed that utilizes deep learning models like VGG16, ResNet50, and DenseNet121 for automatic classification of waste materials. EcoCycle is equipped with a gamification system based on mobile, a marketplace for recyclables supported by blockchain, and an IoT-based network of intelligent bins for real-time monitoring. Experimental results show 92.36% classification accuracy with DenseNet121, which is improved compared to other implementation results. User survey with 500 users shows a 98% positive effect on user experience and increased awareness about sus- tainability issues. The proposed system contributes significantly towards processes related to circular economies and the goals of smart city initiatives, and it has high global applicability potential for urban waste management systems.

Authors

Publication Details

Journal
International Journal of Current Science Research and Review
Published
2025-03-17
DOI
https://doi.org/10.47191/ijcsrr/v8-i3-30
Primary Topic
Municipal Solid Waste Management
Type
article
Field-Weighted Citation Impact
0.00
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article

EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities

Akanksha Rathore, Tuhina Tripathi, Ms.KIRUTHIKA .K -
International Journal of Current Science Research and Review
Municipal Solid Waste Management
article

EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities

Akanksha Rathore, Tuhina Tripathi, Ms.KIRUTHIKA .K -
article en

Abstract

Waste management is a critical environmental and economic issue worldwide. Existing waste segregation ac- tivities are inefficient, resulting in high landfill contributions and environmental contamination. In this paper, an artificial intelligence-based waste categorization and management system, EcoCycle, is proposed that utilizes deep learning models like VGG16, ResNet50, and DenseNet121 for automatic classification of waste materials. EcoCycle is equipped with a gamification system based on mobile, a marketplace for recyclables supported by blockchain, and an IoT-based network of intelligent bins for real-time monitoring. Experimental results show 92.36% classification accuracy with DenseNet121, which is improved compared to other implementation results. User survey with 500 users shows a 98% positive effect on user experience and increased awareness about sus- tainability issues. The proposed system contributes significantly towards processes related to circular economies and the goals of smart city initiatives, and it has high global applicability potential for urban waste management systems.

International Journal of Current Science Research and ReviewVol. 08(03)
Openalex Percentile: Top 93%
Municipal Solid Waste Management
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EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities — Akanksha Rathore, Tuhina Tripathi, et al. · International Journal of Current Science Research and Review (2025) | TGRS Research Map | TGRS