Sneha R. Shegar,
Gosavi Umesh Dattatray,
Lende Arpita Ramdas,
Walunj Trupti Vijay,
- Professor, Department of Computer Engineering, Samarth College of Engineering & Management, Bangarwadi, Belhe, Junnar, Pune, Maharastra, India
- Student, Department of Computer Engineering, Samarth College of Engineering & Management, Bangarwadi, Belhe, Junnar, Pune, Maharastra, India
- Student, Department of Computer Engineering, Samarth College of Engineering & Management, Bangarwadi, Belhe, Junnar, Pune, Maharastra, India
- Student, Department of Computer Engineering, Samarth College of Engineering & Management, Bangarwadi, Belhe, Junnar, Pune, Maharastra, India
Abstract
Waste management and segregation have become a critical issue in the contemporary urban setting, which requires smart and automatic methods. The paper introduces an autonomous waste detection and segregation system, WasteXpert, built on a Raspberry Pi that leverages computer vision and sensor-based classification algorithms. The proposed system utilizes a four-wheel robotic platform with a mechanism for collecting and moving waste, which is a robotic arm. At the same time, a USB camera is used with the YOLOv8 model to detect and track garbage in the scene in real time. When the waste object is detected, the robot drives towards it, picks it up, and places it using a servo-motorized gripper and a DC motor-controlled lifting system. Waste classification is performed using a hybrid sensing method: a metal detector for metallic waste and a conductivity-based copper plate sensor for wet waste. Waste material that fails to meet either of the two conditions is classified as dry waste. It can be controlled in manual mode and automated mode, with manual mode supported through the Android application and autonomous mode enabled for self-driving, object recognition, and decision-making. The use of artificial intelligence combined with embedded system technology will result in a cost-efficient, adaptable, and scalable method for managing municipal solid waste. Experimental findings demonstrate that this technology is effective for both detection and separation regardless of changing environmental conditions. As such, this proposal presents a good option for developing smart waste management solution which leads to cleaner, more sustainable cities
Keywords: Smart waste management, autonomous robot, waste segregation, computer vision, YOLOv8, Raspberry Pi, embedded systems, metal detection, moisture detection
[This article belongs to International Journal of Sustainability ]
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International Journal of Sustainability
| Volume | 03 | |
| Issue | 02 | |
| Received | 10/04/2026 | |
| Accepted | 18/04/2026 | |
| Published | 10/07/2026 | |
| Publication Time | 91 Days |