Waste Segregation and Recycling Guide Using Machine Learning

Authors- Bingi Aswini, Panyam Ramachandra Reddy, M.Chandana Priya, Vaddi Nikith Raj, B. Tejeswar Reddy

Abstract-– The increasing volume of waste generated due to rapid urbanization poses significant challenges to environmental sustainability and public health. Traditional waste segregation methods are often manual, inefficient, and error-prone, highlighting the need for intelligent automation. This paper presents Greencore, an AI-powered system designed to automate waste classification and promote eco-friendly disposal practices. The system employs lightweight machine learning models such as MobileNet and YOLO to accurately classify over 80 types of waste—including recyclable, biodegradable, and hazardous materials—through images captured via a mobile application. Integrated with geolocation services using the Google Maps SDK, the application guides users to the nearest appropriate disposal or recycling facility within a 50-meter radius. Built on a robust backend using FastAPI and MongoDB, the platform also incorporates user behavior tracking, JWT- based authentication, and real-time performance monitoring. Data preprocessing techniques such as normalization and augmentation enhance the accuracy and robustness of the models. The proposed system not only simplifies household and industrial waste management but also contributes to smart city initiatives and circular economy goals by promoting awareness, accountability, and sustainable practices through technology.

DOI: /10.61463/ijset.vol.13.issue2.322