Abstract: In order to improve the efficiency of chemical flower thinning in modern orchards, promote the development of flower thinning machinery, as well as to address the problems of incomplete and low efficiency of traditional identification methods for detecting flowering intensity in orchard production, a data-enhanced YOLOv4 deep learning-based apple flower detection method was proposed. First, a YOLOv4 network model was built with the CSPDarknet53 framework as the backbone feature extraction network, and then offline and online data augmented YOLOv4 methods were investigated in order to reduce the effects of uneven sample data and insufficient quantity. Finally, the artificially annotated apple flower images were used to fine-tune and train the model, and the proposed method was compared with the detection models of YOLOv3, YOLOv4 and Faster R-CNN, after which the detection performance of apple flower under different shooting situations was discussed. The experimental results showed that the AP value of the proposed data-enhanced YOLOv4 method for detecting apple flower was 99.76%. It improved 2.53%, 14.56% and 5.08% over Faster R-CNN, YOLOv3 and YOLOv4, respectively. Compared with other detection methods, this method has higher precision in apple flower detection.