Issuu

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 12 | Dec 2025

p-ISSN: 2395-0072

www.irjet.net

Waste Wise: AI-Powered Recycling Guide with Gamification and Block chain Incentives Preetham V1, Dr. Kavitha A. S2, Akhilesh C. Gowda3, Nithin Gowndar S. P4, Sanath B. G5 1,3,4,5Students, Dept. of Artificial Intelligence and Machine Learning, East West Institute of Technology, Bangalore-

560091, Karnataka, India

2Professor, Dept. of Artificial Intelligence and Machine Learning, East West Institute of Technology, Bangalore-

560091, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract: We built Waste Wise because I got tired of I identified three primary barriers to effective recycling: watching recyclable waste get rejected at sorting facilities due to contamination. The problem is simple: people don't know how to sort waste properly at the moment. I created a mobile app that uses deep learning to instantly identify waste items and tell users exactly how to dispose of them.

My DenseNet201 classifier achieved 98.52% accuracy on a test set of over 7,000 images across 12 waste categories. To make it actually usable on phones, I optimized the model down to 24.5 MB and got inference running in 380 milliseconds on mid-range smartphones using Tensor Flow Lite. User photos never leave the device—all classification happens locally. To keep people engaged long-term, I added two incentive layers. First, a block chain rewards system on Solana distributes $WASTE tokens for each correct scan—I chose Solana because Ethereum's gas fees would cost more than the rewards themselves. Second, gamification mechanics including streaks, badges, and leaderboards tap into daily engagement habits. The system architecture separates concerns cleanly: on-device inference for speed and privacy, Firebase for user management and sync, and Solana for transparent token rewards. Each layer can evolve independently. Early testing showed promising engagement—users came back repeatedly, driven by streak mechanics and accumulating token balances. The combination of instant AI guidance, visible progress tracking, and tangible crypto rewards appears to create stronger engagement than classification alone. Whether this translates to lasting behavior change requires longer-term study. Key Words: Waste Classification, Convolutional Neural Net- works, Transfer Learning, Blockchain Incentives, Mobile Edge Computing, Gamification, TensorFlow Lite, Deep Learning

First, local guidelines vary dramatically across regions and cities, creating genuine confusion even among people who actively want to recycle correctly. Second, traditional awareness campaigns don't reach people at the critical moment—when they're standing in front of a bin making a split-second decision. Third, there's no immediate feedback or incentive that reinforces good sorting behavior, making it hard for people to build sustainable habits. Recent advances in mobile computing and deep learning present new opportunities to address this challenge. Modern smartphones incorporate capable processors and cameras sufficient to run sophisticated image classifiers locally. Con- currently, block chain technology enables transparent, tamper- resistant mechanisms for incentivizing pro-environmental behavior at scale without centralized intermediaries. This paper presents Waste Wise, a mobile application designed to bridge the knowledge and motivation gaps that limit recycling effectiveness. Our work makes the following contributions: 1. We propose a four-tier system architecture that cleanly separates on-device inference, cloud-based user management, and block chain-based incentives. 2. We develop and evaluate a DenseNet201-based waste classifier achieving 98.52% accuracy across 12 categories, optimized for mobile deployment. 3. We implement a token-based incentive mechanism on the Solana block chain where low transaction fees make micro- rewards economically viable. We design a gamification layer incorporating eco-points, achievement progression, and social leaderboards.

1.1 Problem Statement

1. INTRODUCTION During my research into waste management, I quickly realized that the most critical bottleneck in recycling isn't awareness—it's the moment of decision. When someone holds an item and must choose where to dispose of it, they need immediate, reliable guidance.

The primary challenge addressed by this research is the persistent issue of waste contamination in recycling streams due to incorrect sorting by end-users. Traditional educational approaches have proven insufficient to modify behavior at the point of disposal, creating a need for real-time, accessible guide- acne systems.

ISO 9001:2008 Certified Journal

Impact Factor value: 8.315

Page 207