| Summary: | The mango is a popular fruit with over 57 million metric tons produced worldwide. With the rise of artificial intelligence and assistive robotics, this study aims to develop a model and prototype for identifying mango species and their ripeness using a Convolutional Neural Network (CNN), targeting a minimum accuracy of 90%. The research seeks to contribute to agricultural practices and assist consumers, particularly those with visual impairments. The methodology involves capturing images of mangoes, processing them through the YOLOv8 object detection model, and analyzing them with CNN algorithms. The results indicate that the system can successfully differentiate between mango species and their ripeness with a 91.67% accuracy rate. Future work could expand the variety of mangoes and stages of ripeness for a more comprehensive application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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