AI-Based Smart Dustbin for Automated Waste Segregation Using Deep Learning
Year: 2026
Type: article
Abstract: Maintaining environmental sustainability and increasing recycling rates depend heavily on effective waste segregation. Manual labor is a major component of traditional garbage sorting techniques, and it is frequently hazardous, slow, and inconsistent. The design and development of an AI-based smart dustbin that can automatically identify, categorize, and separate waste products is shown in this study. A YOLOv8 deep learning model and a Raspberry Pi 5 are used in the system's construction for real-time image-based garbage classification. An ultrasonic sensor senses the presence of a trash item on the input flap and initiates webcam image capturing. The garbage is classified by the trained model as glass, paper, plastic, metal, or biodegradable. Based on the prediction, a stepper motor rotates the internal bin to the appropriate position, and two SG90 servo motors operate the flap to drop the waste into the correct compartment. Additionally, gas and moisture sensors monitor environmental conditions inside the dustbin to ensure hygiene and safety. The proposed system demonstrates reliable performance and provides a practical solution for smart waste management.
Institution: College of Engineering Kottarakkara
Language: English
Subfield: Waste Management and Disposal
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7134106901