BEST FINAL YEAR ECE PROJECT CENTER IN CHENNAI

 

                    The increasing volume of biomedical waste in hospitals and healthcare facilities poses a serious threat to public health and the environment if not handled and segregated properly. Traditional methods of waste segregation rely heavily on manual labor, which is prone to human error, health risks, and inefficiency. This project proposes an Intelligent Segregation System that combines robotic handling with smart sensors and machine learning to automatically identify, classify, and segregate medical waste. The system uses an ESP32 microcontroller to interface with various sensors including a color sensor, metal detector, and moisture (wet) sensor to determine the type of waste. A robotic arm is used to pick and place the waste into designated bins based on the classification. To enhance accuracy, a TensorFlow-based machine learning model is integrated to classify waste using visual features (optional camera input). The combined use of hardware sensors and AI ensures a safer, faster, and more reliable segregation process. This solution aims to reduce human intervention, minimize health hazards, and promote environmentally responsible waste management in medical facilities.