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Automatic acquisition of plant canopy phenotypic shape is essential for seed selection and scientific cultivation of cucumber varieties. Segmentation accuracy and efficiency are low due to the difficulty of current 3D point cloud processing techniques to perform effective separation of stems and leaves on cucumber plant point clouds. Aiming to address this problem, an improved algorithm for regional growth segmentation and phenotype extraction was proposed by segmented leaves. Firstly, the point cloud data of cucumber was collected from four angles by depth camera, and on the basis of statistical filtering and color filtering to remove the background noise as well as outliers, the complete cucumber plant point cloud by aligning the point cloud-based on rotary axis and generalized nearest point iterative algorithm (GICP), and then the region growth algorithm was improved by using voxel-based and moving least squares algorithms (MLS) to realize the separation of stems and leaves and the segmentation of leaves;finally, phenotypic parameters such as number of leaves, leaf area, leaf length, leaf width, leaf circumference were automatically extracted from the segmented leaf point cloud. The experimental results showed that individual leaves could be accurately segmented by the improved zone-growth algorithm compared with the traditional zone-growth algorithm, with an average increase in accuracy of 12.5 percentage points for 15 d of transplanting and 22.5 percentage points for 60 d of transplanting. The coefficient of determination R2 for the four parameters of leaf area, leaf length, leaf width, and leaf circumference were 0.96, 0.93, 0.93, and 0.94, respectively, and the root-mean-square error RMSE was 12.69 cm2, 0.93 cm, 0.98 cm, and 2.27 cm, respectively, compared with the true measurements. Therefore, the proposed method can efficiently segment individual leaf point clouds from a single cucumber point cloud and accurately calculate related phenotypic traits, providing strong technical support for high-throughput automated phenotypic measurements in greenhouse cucumbers.