AI-Powered Waste Sorting Assistant

Abstract

Abstract The management of solid waste is a critical challenge in India, where rapid urbanization and population growth generate millions of tons of waste annually. Intelligent waste sorting using AI and machine learning (ML) offers a promising solution by automating classification and recycling. This review examines AI/ML-based waste sorting initiatives, with 70% focus on Indian developments and 30% on global research. We survey existing mobile applications – such as D.Waste and CleanIndia – that use on-device ML models (e.g. TensorFlow Lite) and gamification (reward points, leaderboards) to engage users. We analyze technical methods including CNN-based image classifiers, model quantization for edge devices, and Flutter app integration. Performance benchmarks from recent studies are summarized (e.g. ResNet models achieving ~98% accuracy on waste image datasets, and EfficientNet‑B0 reaching ~81–85% with 4× lower computational cost Major challenges in the Indian context are discussed: limited localized datasets, varying lighting and image quality, diverse user behavior, and smartphone hardware constraints. We highlight strategies to improve user adoption, such as reward systems and educational content for schools and communities. The review concludes by outlining future directions (data augmentation, localized datasets, hardware optimizations) and emphasizes the potential of AI-driven waste assistants to support India’s sustainability goals. KEY WORDS: Waste management; Machine learning; Waste sorting; Mobile application; India; TensorFlow Lite; Convolutional Neural Network; Gamification; Smart city; Sustainable behaviour.