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The topography of the orchard is variable. The planting density of the fruit trees is large, and the shape of the crown is different. Therefore, it is difficult to recognize the crown of an individual fruit tree in a complex orchard background. A novel method of crown recognition and segmentation based on Mask R-CNN neural network model was studied. The image data of the citrus orchard was obtained through the camera, and the Mask R-CNN neural network was used to realize the recognition and segmentation of the crown of an individual citrus plant. The research results showed that the recognition accuracy of the individual tree crown of the orchard participating in the modeling was 97%, and the recognition time was 0.26s, which can basically meet the requirements of tree crown recognition in the process of precise orchard operation. The recognition accuracy of the single tree crown of the orchard not participating in the modeling was 89%, which showed that the model was suitable for different kinds and environments of orchards. Compared with the SegNet model, the accuracy of the used model was about 5 percentage points higher, indicating that it had a better recognition and segmentation effect in complex orchard images with more non-target tree crowns. Therefore, the recognition and segmentation method can achieve rapid and accurate recognition and segmentation of single tree crown, which provided an important basis for accurate orchard operations such as target spraying, pest protection, growth recognition and prediction.