Artificial Intelligence Based Waste Segregation System

The surge in urban population has led to a significant rise in municipal solid waste, making effective waste management increasingly important. Conventional methods that rely on manual sorting are often slow, unsanitary, and susceptible to mistakes. To address these challenges, this paper introduces an AI-driven smart waste segregation system that leverages computer vision, deep learning techniques, and IoT technologies to automatically identify and categorize waste materials. The proposed system employs a raspberry pi 4b as its main controller integrated with a USB camera, moisture detection sensor, and ultrasonic sensor. A convolutional neural network built on the MobileNetV2 framework is trained using a dataset of around 3153 images to distinguish between two types of waste: biodegradable (wet) and non-biodegradable (recyclable). After identification the waste is automatically sorted into the correct container using a mechanism that combines servo and stepper motors ensuring accurate placement and better operational control. The model demonstrated a testing accuracy of 92%. Additionally the system supports real-time monitoring of bin levels and sends notifications when bins are full through the Blynk IoT mobile application. The proposed system reduces human intervention, improves segregation accuracy, and supports sustainable waste management, making it suitable for smart cities and public institutions in alignment with the Swachh Bharat Abhiyan and UN Sustainable Development Goals.