The center coordinate and radius of the spherical hedges are the basic phenotypic features for automatic pruning. A binocular vision-based shape reconstruction and measurement system for front-end vision information gaining are built in this paper. Parallel binocular cameras are used as the detectors. The 2D coordinate sequence of target spherical hedges is obtained by region segmentation and object extraction process. Then, a stereo correcting algorithm is conducted to keep two cameras to be parallel. Also, an improved semi-global block matching (SGBM) algorithm is studied to get a disparity map. According to the disparity map and parallel structure of the binocular vision system, the 3D point cloud of the target is obtained. Based on this, the center coordinate and radius of the spherical hedges can be measured. Laboratory and outdoor tests on shape reconstruction and measurement are conducted. In the detection range of 2,000–2,600 mm, laboratory test shows that the average error and average relative error of standard spherical hedges radius are 1.58 mm and 0.53%, respectively; the average location deviation of the center coordinate of spherical hedges is 15.92 mm. The outdoor test shows that the average error and average relative error of spherical hedges radius by the proposed system are 4.02 mm and 0.44%, respectively; the average location deviation of the center coordinate of spherical hedges is 18.29 mm. This study provides important technical support for phenotypic feature detection in the study of automatic trimming.

Automated pruning is an inevitable trend in the improvement of modern gardens. In order to provide necessary information for automatic garden robots and satisfy the requirement of target detection and positioning during pruning, this paper proposed a bush spherical center detection algorithm based on a 3D depth camera point cloud. Firstly, the depth camera collected the bush image, and the results were aligned to the depth image to obtain the 3D point cloud of bush. Then the ROI was extracted by preprocessing, and the 3D point clouds of bush was obtained after filtering and coordinate transformation. Finally, the spherical center coordinates of the bush were extracted by the minimum bounding box method. Four groups of tests on the bush spherical coordinates detection were carried out outdoors. The maximum location error and the minimum location error of the spherical bush center were 10.23mm and 8.65 mm, respectively, and the average location error was 9.51mm. The bush spherical center detection algorithm based on depth camera 3D point clouds proposed in this paper provides a technical reference for the information acquisition of automatic pruning robot.