A Mobile Based Waste Reporting and Analysing Through Machine Learning Technologies

Author: Akkireddi Vara Prasad, V. Bharath Sai, S. Sundar Raju, K. Gnaneswara Rao And V. Kiran Kumar

Abstract

The rapid growth of urban populations has led to a significant increase in waste generation, creating challenges in effective waste management and environmental sustainability. This project proposes a mobile-based waste reporting and analysis system that leverages machine learning technologies to improve the efficiency of waste collection and monitoring processes. The application enables users to capture and upload images of waste in their surroundings, along with location data, to report uncollected garbage in real time.

The system utilizes machine learning algorithms, particularly in the field of Computer Vision, to automatically classify types of waste such as biodegradable, non-biodegradable, and recyclable materials. Additionally, predictive analytics techniques are employed to identify waste accumulation patterns and forecast high-risk areas requiring immediate attention. This helps municipal authorities optimize collection routes and allocate resources more effectively.

The mobile platform integrates GPS functionality, cloud storage, and data visualization dashboards to provide actionable insights for both users and waste management authorities. By promoting citizen participation and enabling data-driven decision-making, the proposed system aims to reduce environmental pollution, enhance cleanliness, and support the development of smart and sustainable cities.

Keywords: Mobile Application, Waste Management, Machine Learning, Computer Vision, Image Classification, Smart City, Waste Reporting, Predictive Analytics, Data Visualization, Environmental Sustainability, GPS Tracking, Cloud Computing, Urban Sanitation, Garbage Monitoring, Recycling Detection.