<![CDATA[Waste management in a university setting is a complex task that involves handling diverse waste streams in classrooms, laboratories, and canteen areas, proper segregation of wastes, and disposal while ensuring compliance with environmental regulations and sustainability goals. In response to the inefficiencies in handling adequate waste segregation at Guimaras State University (GSU), this study presents the development of a Smart Waste Management System using Computer Vision. The system aims to develop a model to classify waste according to waste type, handle automatic segregation, and provide stakeholders with real-time updates. Adopting the Cross Industry Standard Process for Data Mining (CRISP-DM) is part of the approach, including business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases. The complications of waste management processes have been identified through interviews with utility personnel at Guimaras State Unversity – Mosqueda Campus. A prototype bin was developed using hardware electronic technologies and third-party platforms such as Edge Impulse and Blynk to handle waste identification for proper disposal and segregation. The study’s primary results include a substantial decrease in the complexity of the waste management process, as well as promoting adequate waste segregation. By utilizing hardware technologies and training a machine learning model, the system was able to manage waste identification, automated waste disposal operations, and offer real-time updates to the stakeholders. Overall, the Smart Waste Management System using Computer Vision offers efficiency and sustainability in the waste management process at GSU. Keywords: smart waste management; computer vision; arduino; esp32 camera; esp32]]>
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