IOT based Waste Segregation Using Deep Learning

Abstract

In the rapidly urbanizing world, effective waste management is crucial to maintain environmental sustainability. This project presents an innovative solution utilizing Internet of Things (IoT) technology and deep learning Convolutional Neural Networks (CNN) algorithm for automated waste segregation. We propose a smart waste segregation system equipped with sensors to detect the level of waste in bins. The system utilizes a CNN model trained using Deep Learning to classify waste items into different categories, ensuring proper segregation. When a bin reaches its full capacity, the IoT sensors trigger an alert mechanism, sending real-time notifications to the concerned authorities or waste management personnel. This proactive approach not only optimizes waste collection but also promotes recycling by ensuring the segregated waste reaches the appropriate recycling facilities. The integration of IoT devices and deep learning algorithms enhances the efficiency of waste management processes, reducing environmental pollution and promoting a sustainable future. Key Words: IoT, waste management, deep learning, CNN algorithm, waste segregation, environmental sustainability, real-time monitoring, sensor technology, recycling, smart cities, automation.