Exploring the Application of Machine Learning in the Field of Fruit Image Recognition

Abstract

Image recognition techniques play an important role in business and science fields. The application of these techniques, especially the YOLO algorithm's real-time processing capabilities and high accuracy make it particularly noteworthy in advancing fruit image recognition. This study focuses on the application of machine learning in the field of image recognition, and in particular explores the use of the YOLO (You Only Look Once) algorithm in fruit image recognition. The study experimentally explores the application of the YOLO algorithm to a complex fruit recognition task, with a focus on evaluating the algorithm's real-time processing power and accuracy. Using a wide range of fruit image datasets including COCO, OID and KITTI, the YOLO model was trained and tested with the aim of gaining insights into the algorithm's performance in different real-world scenarios and its limitations. This research points out the importance of continuous model improvement, especially in terms of image quality, model adaptation, and with other techniques such as NLP. Directions for future research include the development of more transparent and interpretable deep learning models.