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The traditional method of artificial seedling phenotype measurement has some problems, such as low efficiency, strong subjectivity, large error and damaged seedlings. A method for nondestructive detection of cucumber seedling phenotype by using the RGB-D camera was proposed. An automated multi-view image acquisition platform was developed, and two Azure Kinect cameras were deployed to simultaneously capture color, depth, NIR, and RGB-D images from the top view and side view. The Mask R-CNN network was used to segment the leaves and stems in the NIR image, and then mask them with the RGB-D image to eliminate the background noise and ghost in the RGB-D images and obtain the RGB-D image of the leaves and stems. The category and number of segmentation results of the Mask R-CNN network were the numbers of cotyledons and true leaves. The CycleGAN network was used to process the RGB-D image of a single leaf, repair the missing and convert it into 3D point clouds, and then filter the point clouds to achieve edge-preserving denoising. Finally, the point clouds were triangulated to measure the leaf area. In the stem RGB-D image obtained by Mask R-CNN segmentation, the approximate rectangular feature of the stem was used to calculate the length and width of the stem respectively, and then the depth information was combined to convert the hypocotyl length and stem diameter. YOLOv5s was used to detect the growing point of cucumber seedlings in the RGB-D image, and the height difference between the growing point and the substrate was used to calculate the plant height. The experimental results showed that the system had good flux and accuracy. The mean absolute errors of key phenotypes of cucumber seedlings at cotyledon, 1 true-leaf and 2 true-leaf stages were all no more than 8.59% and R2 was no less than 0.83, which can well replace the manual measurement method, and provide key basic data for seed selection and breeding, cultivation management, growth modeling, and other research.