Fresh/Rotten Fruit Detection Using Raspberry Pi

The Fresh/Rotten Fruit Detection system operates as an edge-based computer vision and machine learning pipeline powered by a Raspberry Pi 5 development board. The process begins when the connected camera or webcam captures real-time video frames of the fruit being presented to the system. These image frames are ingested into a Python environment, where OpenCV handles preliminary image processing, formatting, and scaling to match the input requirements of the machine learning model. A custom classifier trained via Google Teachable Machine or Lobe utilizes TensorFlow to evaluate the visual features of the fruit, comparing patterns, colors, and surface textures against trained classes representing fresh and rotten states. Once the model outputs a classification prediction with a defined confidence score, the Raspberry Pi interprets the result. If an optional sorting mechanism is integrated, the system can generate control signals to drive a servo motor or actuator, physically routing the fruit into the appropriate bin based on its quality classification. Throughout this cycle, the Raspberry Pi executes both the high-level neural network inference and the hardware-level GPIO actuation concurrently, creating a fully autonomous classification node.