SwachhSnap: Smart Waste Monitoring and Reporting Using AI-Powered Mobile Application
Keywords:
AI, Citizen engagement, Convolutional neural networks (CNNs), Mobile application, Route optimization, Smart waste managementAbstract
Rapid urbanization and industrial development have led to a significant increase in municipal solid waste, posing environmental, public health, and operational challenges worldwide. Conventional waste management systems, relying on manual monitoring, scheduled collection, and citizen complaints, are often reactive and inefficient. This paper presents SwachhSnap, an AI-powered mobile application framework for real-time waste monitoring, automated classification, and efficient municipal reporting. The proposed system utilizes Convolutional Neural Networks (CNNs) to classify waste into biodegradable, recyclable, hazardous, and electronic categories. GPS tracking enables precise location identification, while a cloud-based dashboard is designed to provide heatmaps, priority alerts, complaint tracking, and optimized collection routes using algorithms like Dijkstra’s and A* search. SwachhSnap encourages citizen participation through gamification and rewards. Based on current deep learning literature, the system is projected to achieve a classification accuracy exceeding 90% and is designed to enable a significant reduction in response time through data-driven route optimization. The system is scalable for integration with IoT-enabled smart bins, predictive analytics, and municipal ERP systems, offering a proactive solution for modern urban waste management and supporting initiatives like India’s Swachh Bharat Mission.
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