The Mask R-CNN is a relatively advanced instance segmentation model that has been applied in the field of intelligent agriculture wildly, such as fruit detection [26], pest detection [27], evaluating ecological patterns [28], and more. Most relevant to this article is its successful application to challenging forestry tasks. For example, [PERSON] et al. [29] used the Mask R-CNN to recognize and segment olive tree crowns and estimate a single tree's biological volume. Accuracy of the model ranged from 77 to 95%. [PERSON] et al. [30] achieved the simultaneous detection of Chinese fir crowns and tree heights using the method, but the precision of the detection was around 85%. [PERSON] et al. [31] proposed a coniferous crown segmentation and recognition method based on their improved Mask R-CNN version. However, the accuracy was improved by less than 1%. With their boundary segmentation algorithm, several geometric parameters (e.g., contour, center of gravity, and area) were amazingly extracted. Comparisons were later made with highly specialized task-specific models, such as UNet [32], which had made great advances in image-training efficiency for medical decision support, and You Only Look Once version three (YOLOv3) [33], which had a huge breakthrough in object detection. For instance, [PERSON] et al. [34] proposed a modified YOLOv3 model to detect potholes accurately on the pavement surface. Compared with the original YOLOv3 model, the proposed model was significantly improved [35]. Different versions of YOLO models were combined with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement by [PERSON] et al. [36]. The Mask R-CNN astonished everyone by outperforming those and other high-profile tools in some important metrics and had high precision and strong robustness. Additionally, Mask R-CNN, an instance segmentation method, has the function of segmentation and detection. Therefore, it is more suitable for segmenting individual tree crowns than semantic segmentation methods. In summary, current tree crown segmentation methods have the following shortcomings: it is difficult for RGB camera systems to identify specific spraying objects from many candidates [13; 14; 15]; they are not robust enough to achieve good performance when complex backgrounds are present [18; 19; 20]; most of the original Mask R-CNN is used for crown segmentation; and there is still significant room for improved accuracy [29; 30; 31]. Therefore, the goal of this paper is to provide the most precise and accurate citrus crown segmentation tool available, and we accomplish this. Our key contributions are as follows:  
* For improved object identification, RGB-D images are collected, and the image noise outside the effective spraying area is removed by aligning pixels and adjusting the visual distance. Compared to that of RGB images, the bbox AP50 score is improved by 0.3% for RGB-D images. * To increase robustness in complex backgrounds, the model is trained using citrus crown images with different backgrounds at different growth stages. The model's bbox and seg AP50 indicators are averaged over 95%, indicating a good overall performance and strong generality. * To improve the accuracy of tree crown segmentation, an improved instance segmentation method based on the Mask R-CNN framework is proposed. The UNet++ is a commonly used semantic segmentation network [37]. We employ a feature map-based SE block (a neural network that can improve the feature extraction ability) with UNet++ (MSEU) in the R-CNN. The SE block is integrated with the residual network (ResNet) [38; 39] to improve the extractability of tree crown features, and the UNet++ is introduced in the mask branch (a neural network used for segmenting images) to further improve segmentation quality. Compared with those of the optimal Mask R-CNN, the bbox and seg AP50 of MSEU R-CNN were improved by 3.4% and 2.4%, respectively. The remainder of this paper proceeds as follows.