Waste Segregation through AI
Abstract
Growing volumes of municipal solid waste, driven by rapid urbanization, have intensified demands for scalable and intelligent material classification tools. This paper introduces a purely browser-based waste sorting application that leverages Artificial Intelligence to categorize discarded items into three groups: Biodegradable/Wet, Recyclable/Dry, and Hazardous. The system deploys a TensorFlow.js inference model trained via Google Teachable Machine, executing all predictions locally within the user’s browser without transmitting any data to an external server. The front-end stack, built with HTML5, CSS3, Tailwind CSS, and Daisy UI, supports live webcam capture and static image uploads, delivering results within two to five seconds accompanied by color-coded disposal advisories. An embedded feedback channel enables users to flag incorrect predictions, supporting iterative model improvement. Testing across multiple browsers and device categories confirmed reliable classification performance, with plastic materials achieving confidence levels approaching 97.6%. The application is lightweight, privacy-conscious, and deployable without backend infrastructure, making it suitable for municipal, educational, and community-level waste management programs.
References
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[6] United Nations Environment Programme, Solid Waste Management: Global Trends and Regional Guidelines. Nairobi: UNEP, 2019.
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