In recent years, artificial intelligence has been widely used in various fields. To address time-consuming manual weighing and complicated pricing procedures in supermarkets and vegetable markets, this study proposes an automatic fruit recognition model based on attention YOLOv5. First, to improve the recognition accuracy of fruits with different local features but similar global features, the study adds squeeze-and-excitation networks (SENet) after the spatial pyramid pooling (SPP) layer of YOLOv5 and uses the attention mechanism to automatically learn the importance of each feature channel. Further, the useful features for fruit recognition tasks according to the importance are strengthened and those useless are suppressed. Second, when the fruit recognition prediction frame overlaps the target frame, GIOU cannot accurately express the overlapping relationship of the frames. In response, this study replaces the original frame regression loss function GIOU with CIOU and considers the relationships of aspect ratio and center point between the target frame and the prediction frame. In this way, the fruit prediction frame is closer to the real frame, and thereby the prediction accuracy is improved. Experimental results show that the improved model has significantly improved fruit recognition ability in common scenarios with a mean average precision (mAP) of 99.10% and a recognition speed of 82 FPS, which can meet the needs of practical applications.