Smart Waste Sorting System using AI

In today's world, efficient waste management is crucial for environmental sustainability. This project proposes an automated waste segregation system using deep learning, computer vision, and embedded systems to classify waste into biodegradable and non-biodegradable categories. A Convolutional Neural Network (CNN) model is trained using a dataset of biodegradable and non-biodegradable waste images. The model is deployed with Python and OpenCV to automatically classify waste in real-time. The system is integrated with an embedded hardware setup that includes a NodeMCU microcontroller, an LCD display, and two ultrasonic sensors to monitor waste bin levels. Classified waste is directed to the appropriate bin, and the system continuously checks the bin levels, providing real-time updates via the Blynk IoT platform. When the bins approach their capacity, notifications are sent to users to ensure timely waste disposal. This smart waste segregation system aims to enhance waste management efficiency by reducing human intervention and promoting the proper disposal of waste, contributing to a cleaner environment.