Intelligent pruning is an effective way to improve the efficiency of fruit tree pruning and reduce production costs, where the positioning of fruit tree pruning points is the key. In this study, a pruning point localisation method based on deep learning and red-green-blue-depth (RGB-D) is proposed for dormant tall spindle apple trees, which can quickly and accurately identify the pruning points on the primary branches. Firstly, red-green-blue (RGB) images and depth images of apple trees were acquired by using the Realsense D435i depth camera, and the SOLOv2 instance segmentation model was used to segment the trunks, branches, and supports in RGB images. Secondly, the manual pruning rules were adapted to improve the pruning methods; then using OpenCV image processing method, the junction points of branches and trunk and potential pruning points were gained, and the world coordinates of the points were obtained according to the coordinate transformation to calculate the length of branch diameter and spacing. Finally, the pruning points were determined according to the pruning rules. The results show that SOLOv2 has better segmentation effect compared with Mask R–CNN and Cascade Mask R–CNN. The mean absolute error between the estimated and manually measured values of branch diameter and spacing are 1.10 mm and 16.06 mm, and the recognition accuracy of pruning points is 87.2%, with a recognition time of about 3.8 s for each image. It is shown that the method can measure branch diameter and spacing and quickly locate pruning points with high reliability and accuracy, and the study provides a basis for the development of apple tree pruning robots.

智能修剪是提高果树修剪效率、降低生产成本的有效途径，其中果树修剪点的定位是关键。本研究提出了一种基于深度学习和红绿蓝深度（RGB-D）的休眠高纺锤体苹果树修剪点定位方法，能够快速、准确地识别主枝上的修剪点。首先，使用Realsense D435i深度相机获取苹果树的红绿蓝（RGB）图像和深度图像，并使用SOLOv2实例分割模型对RGB图像中的树干、树枝和支撑物进行分割。其次，采用人工剪枝规则，改进剪枝方法；然后利用OpenCV图像处理方法，得到树枝与主干的交接点和潜在修剪点，并根据坐标变换得到这些点的世界坐标，计算出树枝直径和间距的长度。最后根据剪枝规则确定剪枝点。结果表明，SOLOv2 与 Mask R-CNN 和 Cascade Mask R-CNN 相比具有更好的分割效果。枝条直径和间距的估计值与人工测量值之间的平均绝对误差分别为1.10 mm和16.06 mm，修剪点的识别准确率为87.2%，每幅图像的识别时间约为3.8 s。实验结果表明，该方法能够测量枝条直径和间距，快速定位修剪点，可靠性和准确性较高，为苹果树修剪机器人的开发提供依据。