Abstract: Each organ can effectively represent the health degree of cotton. It is also required for the physiological and phenotypic analysis of each organ, in order to monitor the growth and development status of cotton plants. However, the three-dimensional point cloud makes it difficult to segment newborn leaf organs at the top of the cotton seedling stage. In this study, a newborn leaf segmentation was proposed to combine Laplacian skeleton extraction and Quickshift++ using a distance field. The five steps mainly included: skeleton extraction, skeleton decomposition, new leaf extraction, new leaf organ segmentation, and phenotype parameter extraction. In skeleton extraction, the Laplace algorithm was used to extract the skeleton of a cotton plant. In skeleton decomposition, the plant skeleton was decomposed into stem and partial leaf subskeleton, according to the morphological characteristics of the cotton plant. The coordinate system of plant space was then established. The Z-axis of the coordinate system was overlapped with the stem axis, in order to better represent the typical morphology of cotton plants. The point cloud of the cotton plant was transformed from the original to the spatial coordinate system of the cotton plant. Subsequently, the skeleton decomposition was carried out in the spatial coordinate system of the cotton plant. A distance field of organ segmentation was also proposed to accurately segment newborn leaf organs using Quickshift++. The reason was that the Laplacian algorithm was limited to extracting new leaf skeletons, easily leading to over-segmenting newborn leaves, especially for the cotton plants with newborn leaves and the very small distance among them. As such, the whole organ segmentation of the cotton plant was realized at the seedling stage. The global spatial features of the newborn leaf were also encoded to rapidly locate the precise segmentation of the organ. In the segmentation of newborn leaves, the Quickshift++ algorithm with distance field as the input was first used to extract the tip point clouds of different organs of newborn leaves. Secondly, the neophylla apex cloud was identified using the local geometric features. Then the stem point cloud of newborn leaves was segmented to combine with the median normalized vector growth segmentation. Finally, the Quickshift++ algorithm was used to segment the point cloud of newborn leaf organs. The results showed that the average accuracy of organ segmentation was 0.977, the average recall rate was 0.978, the average micro F1 score was 0.976, and the average overall accuracy was 0.983. The determination coefficients of stem height, leaf width, leaf length, and leaf area were 0.982, 0.962, 0.971, and 0.949, respectively. The root mean square errors were 0.119 cm, 0.182 cm, 0.163 cm, and 1.458 cm², respectively. The improved model can accurately segment the cotton plant organs at the seedling stage. The findings can also provide an effective technical means for the high-throughput phenotypic detection of cotton.