• Rohith D Yadav
• Rakshita MB
• Sinchana SS
• Pranav Rajath
• Dr. Rekha N

This study presents an automated biomedical waste management system that integrates a robotic arm with YOLOv9-based real-time image recognition and UV-C sterilization. A camera module captures waste items such as gloves, syringes, vials, and glass bottles, which are detected and classified using the YOLOv9 deep learning model trained in Google Colab. Prior to sorting, the waste is sterilized using UV-C light to neutralize harmful pathogens. Based on the AI output, an Arduino Uno or Raspberry Pi controls the robotic arm to perform precise pick-and-place operations, segregating waste into appropriate color-coded disposal bins. By combining artificial intelligence, robotics, and sterilization technology, the system minimizes human intervention, improves sorting accuracy and efficiency, and significantly reduces infection risks. The proposed solution supports Sustainable Development Goal 12 (Responsible Consumption and Production) by enabling safer, more efficient, and sustainable biomedical waste handling in healthcare environments

Robotic arm, servo motors, mobile camera, waste sorting bins, yolo V9, roboflow, Arduino IDE, python

"ROBOTIC ARM FOR BIOMEDICAL WASTE SORTING USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.d351-d358, December-2025, Available :http://www.jetir.org/papers/JETIR2512346.pdf

"ROBOTIC ARM FOR BIOMEDICAL WASTE SORTING USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppd351-d358, December-2025, Available at : http://www.jetir.org/papers/JETIR2512346.pdf