Xiao, BingjieNguyen, MinhYan, Wei Qi2023-06-122023-06-122023-06-10Multimedia Tools and Applications, ISSN: 1380-7501 (Print); 1573-7721 (Online), Springer Science and Business Media LLC. doi: 10.1007/s11042-023-15938-11380-75011573-7721https://hdl.handle.net/10292/16240We describe a non-destructive test of apple ripeness using digital images of multiple types of apples. In this paper, fruit images are treated as data samples, artificial intelligence models are employed to implement the classification of fruits and the identification of maturity levels. In order to obtain the ripeness classifications of fruits, we make use of deep learning models to conduct our experiments; we evaluate the test results of our proposed models. In order to ensure the accuracy of our experimental results, we created our own dataset, and obtained the best accuracy of fruit classification by comparing Transformer model and YOLO model in deep learning, thereby attaining the best accuracy of fruit maturity recognition. At the same time, we also combined YOLO model with attention module and gave the fast object detection by using the improved YOLO model.http://creativecommons.org/licenses/by/4.0/0801 Artificial Intelligence and Image Processing0803 Computer Software0805 Distributed Computing0806 Information SystemsArtificial Intelligence & Image ProcessingSoftware Engineering4009 Electronics, sensors and digital hardware4603 Computer vision and multimedia computation4605 Data management and data science4606 Distributed computing and systems softwareJournal ArticleOpenAccess10.1007/s11042-023-15938-1