Fruit Sorting Using OpenCV on Raspberry Pi

This automated fruit sorting system operates through an integrated loop of computer vision and electromechanical control centered around the Raspberry Pi 4 Model B and Raspberry Pi camera. When fruit passes before the camera, the system captures real-time image frames using the PiCamera library within a Python 3 environment running on Raspbian OS. The captured data is processed through OpenCV and NumPy for pixel manipulation and feature extraction, while advanced software packages like TensorFlow and Dlib handle the machine learning inference needed to classify the fruit type or quality based on visual features. Once the software determines the category of the fruit, the Raspberry Pi utilizes the gpiozero library to generate precise control signals. These signals are sent via connecting wires to a servo motor, which physically actuates a mechanical guide or sorting gate to direct the fruit into the appropriate bin or conveyor path. A dedicated power supply ensures stable electrical current to both the high-processing Raspberry Pi board and the moving mechanical parts of the servo motor during continuous operation.