ML-Based Web Application for Sustainable Waste Reuse Suggestions
Year: 2025
Type: article
Abstract: Effective waste management is crucial for maintaining a clean and healthy environment. Traditional wastedisposal methods often result in inefficiencies, leading to pollution and health hazards. This paper presentsa Machine Learning-Based Waste Classification and Reuse Suggestion System integrated with Geo-Taggingand Automated Email Notifications. The web application enables users to upload images of waste, whichare classified into categories such as plastic, metal, glass, paper, and organic waste using a ConvolutionalNeural Network (CNN). Based on classification results, the system provides reuse suggestions and relevantexternal links, promoting sustainable waste practices. Additionally, users can report waste accumulationsites using a Geo-Tagging Module powered by Leaflet.js and the OpenCage API, which captures locationcoordinates and displays them on an interactive map. A Flask-Mail-based Automated Notification Systemsends detailed reports to that area's municipal authorities, ensuring immediate action. The model is trainedon a public dataset and demonstrates high classification accuracy. By integrating machine learning,geospatial reporting, and real-time communication, this system enhances waste management efficiency.
Author: Dundi Harini, Gannoji Chikitha, Shaik Fatima, Gnyana Deepa
Language: English
Subfield: Waste Management and Disposal
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949384847
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