Designing and Implementing a Classification Model for Mangoes Based on Size and Ripeness using Image Processing

Image processing is an advanced technology that significantly supports production, identification, and quality control for fruits. This paper uses image processing techniques to develop a mango classification system based on size and ripeness. The system integrates hardware, including an Arduino microcontroller, camera, sensors, actuators, and a user-friendly computer interface for monitoring and control. The classification algorithm extracts key features of the mangoes, such as their color and shape, to categorize them into predefined quality classes. Experimental results demonstrate that the system achieves an accuracy exceeding 90% for both ripeness and size classification, with a productivity level of 300 kg/hour, surpassing the initial target of 250 kg/hour. Furthermore, the system operates reliably under varying lighting conditions, ensuring flexibility and continuous productivity. These advancements highlight the system’s potential to enhance efficiency and quality in fruit processing industries.