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GRINIFY: AI-POWERED WASTE SORTING AND MANAGEMENT


EOI: 10.11242/viva-tech.01.09.45

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Citation

Prof. Vaishali Shimpi, Sarthavi Birje, Gauravi Mestry, Sarthak Patil " GRINIFY: AI-POWERED WASTE SORTING AND MANAGEMENT ", VIVA-IJRI Volume 1, Issue 9, Article 45, pp. 1-15, 2026. Published by Artificial Intelligence And Machine Learning Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The rapid increase in urban populations and industrial activities has intensified global waste generation, leading to severe environmental, economic, and public health challenges. Improper disposal and inadequate segregation of waste contribute to pollution, landfill overflow, and elevated greenhouse gas emissions, necessitating innovative technological solutions for sustainable waste management. While conventional waste management systems rely heavily on manual sorting or specialized hardware, these approaches are often labor-intensive, inaccessible, and difficult to scale for widespread adoption. Recent research has demonstrated the potential of web-based platforms combined with artificial intelligence to automate waste classification, provide actionable feedback, and engage users in eco-friendly behavior. Grinify is a web-based waste sorting and management system designed with a clean and intuitive user interface that leverages computer vision and deep learning techniques to classify waste into bio-degradable, non- biodegradable, reusable, recyclable, and non-recyclable categories in real time. The platform integrates an analytics dashboard that visualizes user performance, tracks daily, weekly, and monthly challenges, and quantifies environmental impact through metrics such as estimated CO₂ reduction. In addition, Grinify incorporates gamification mechanisms, including points, leaderboards, and achievement badges, to incentivize active participation and sustained engagement among users.The system addresses key limitations observed in previous solutions, such as dependency on specialized devices, lack of real-time feedback, and limited user engagement features. By providing a browser-accessible interface, Grinify ensures accessibility across devices and demographic groups, allowing users, communities, and educational institutions to participate in responsible waste management practices. Furthermore, the integration of environmental analytics with gamification not only enhances user motivation but also promotes awareness of the ecological benefits of proper waste segregation.This paper provides a comprehensive review of existing web-based and AI-driven waste management systems, identifies persistent gaps in real-time classification and user engagement, and presents the design and implementation of Grinify as a scalable, user-friendly, and environmentally impactful platform.The proposed framework demonstrates the feasibility of combining intelligent automation, behavioral incentives, and web accessibility to. promote sustainable waste management practices at both individual and community levels.

Keywords

- Grinify, Waste Sorting, Waste Management, Web‑Based System, Computer Vision, Deep Learning,Waste Classification, Gamification, User Engagement, Analytics Dashboard, Environmental Impact, CO₂Reduction, Sustainable Behavior, Intelligent Waste Systems, Digital Incentives, Real‑Time Feedback,UX Design..

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