Yuvraj Ruparel^1,, Lavkush Chaudhary², Anuj Rathod²

The increasing generation of municipal solid waste (MSW) poses significant environmental challenges, necessitating advanced and sustainable waste management solutions. This study introduces an AI-powered robotic sorting system designed to automate and optimize waste classification processes. The system integrates cutting-edge deep learning techniques, particularly the VGG16 model, with robust hardware components such as the Raspberry Pi 4 and Logitech C920 camera to achieve highly accurate waste segregation. Real-time image processing and precise classification algorithms enable the system to distinguish between wet, dry, and electronic waste with an impressive accuracy of up to 98%. By minimizing the need for human intervention, the proposed system enhances sorting efficiency, improves material recovery rates, and addresses key inefficiencies in traditional waste management practices. This innovation not only supports the reduction of landfill dependency but also promotes environmental sustainability by optimizing resource utilization and reducing ecological footprints. The findings highlight the potential of AI-driven systems to transform waste management, offering a scalable and effective approach to mitigating the environmental impacts of MSW.

Waste management, artificial intelligence, deep learning, robotic sorting, VGG16, waste classification, smart bin