AI enabled intelligent waste systems for sustainable waste management

Abstract

Rapid urbanization, overflowing landfills and changing consumption patterns have enormously intensified challenges in municipal solid waste management, particularly with respect to segregation inefficiencies, contamination of recyclables, and limited recovery of low-category value plastics. While artificial intelligence, smart sensing technologies and data-driven systems have improved the backend waste operations, their effectiveness remains constrained by poor segregation at the point of generation that is at the household levels and low citizen engagement. This paper addresses the function of intelligent waste systems in the lifecycle of waste, focusing on the need for articulation of technological solutions with active participation by civilians. It introduces a user-centric digital prototype called PlastiStar, which is delivered via a web-based mobile application. It combines AI-assisted plastic identification, QR-based item logging, and incentive-driven engagement in support of household-level segregation and traceability of low-value plastics. The argument advanced here is that the potential of intelligent waste systems will result in sustainable outcomes only when automation, data transparency, and citizen involvement work coherently together as part of one unified ecosystem and not as discrete, isolated parts of it.