Smart Garbage Management System
DOI:
https://doi.org/10.15662/IJEETR.2026.0802175Keywords:
Smart Waste Management, Intelligent Garbage System, Automated Waste Segregation, IoT-Basedsolutions, ESP32 Microcontroller, Metal Detection Sensor, Ultrasonic Sensor (Level Monitoring), LCD Display, Waste Classification (Wet / Dry / Metal), Fill Level DetectionAbstract
The Smart Garbage Management System is an intelligent waste segregation and monitoring solution designed using an ESP32microcontroller and multiple sensors. The system automatically classifies waste into wet, dry, and metal categories using a gas (smell) sensor and a metal detection sensor. An ultrasonic sensor is employed to continuously monitor the garbage level inside the dustbin and determine whether it is full or not.
To enhance user interaction, an 12C-based LCD display is integrated, which shows real-time information such as waste type detected, bin fill percentage, and system status. The ESP32 enables loT-based monitoring by transmitting data to a cloud platform, allowing remote supervision through a mobile or web interface. This system minimizes human intervention, promotes hygienic waste disposal, and supports smart city initiatives by improving efficiency in garbage collection and segregation
References
1. Anbalakan S., Automated waste segregation using sensor-based detection system, 2020.
2. Gayathri K.S., Automatic waste segregator and monitoring system using Arduino, 2019.
3. Islam M.M., Smart waste management system using Internet of Things technology, 2018.
4. Basavaraju S.R., IoT-based smart waste management system for efficient collection, 2017.
5. Kale S.K., Design and development of automatic waste segregator system, 2019.
6. Kumar A., Waste management using machine learning techniques for classification, 2020.
7. Khandare R.S., Arduino based automatic waste segregator using sensors, 2018.
8. Yang J., WasteNet deep learning model for waste classification accuracy, 2020.
9. Prabha, S. P., & Rengarajan, A. (2025). ENHANCING CLOUD RESOURCE ALLOCATION WITH VISION TRANSFORMER, DEEP REINFORCEMENT LEARNING, AND IMPROVED SHRIKE OPTIMIZATION ALGORITHM. Corrosion Management ISSN: 1355-5243, 35(2), 233-245.
10. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
11. Sahid, M. H., Pratama, D. A., Abd Rahman, M., Vardhani, A. K., Kulsum, D. U., Tanaka, J., ... & Renaldi, T. (2026). Kesehatan Masyarakat Di Era Digital. CV Eureka Media Aksara.
12. Rajasekar, M., Mukil, A., & Lakshamanan, R. (2024, August). Segmentation and evaluation of multiple sclerosis in flair modality MRI with ResUNet. In AIP Conference Proceedings (Vol. 3161, No. 1, p. 020314). AIP Publishing LLC.
