Cotton is an important agricultural crop in China, which is related to national economy and people’s life. The production, consumption and import of cotton in China always keep the front place in the world. Cotton leaves are the main organs controlling photosynthesis and transpiration, and the seedling leaves have significant influence on cotton yield and disease resistance. Therefore, accurate quantification of cotton seedling leaf traits is necessary and helpful for the cotton breeding, disease resistance research and functional gene mapping. However, the traditional method for the leaf traits investigation is generally manual measurement, which is laborintensive, subjective, and even destructive. To solve the problem, a novel method was demonstrated to extract cotton seedling leaf traits from 3D point cloud based on structured light imaging. In the study, the 3D point cloud data, including color information was acquired by the structured light scanner. Specific point cloud processing pipeline was developed to identify each leaf, by applying passthrough filtering, super voxel and conditional Euclidean clustering algorithms. Based on the segmented leaf point clouds, the leaf traits, including leaf area, leaf perimeter, leaf angle, leaf rolling degree and leaf yellow ratio were extracted accurately by using triangular patches generation, random sampling consensus, and Lab color space segmentation algorithms. To evaluate this method, 40 cotton plants treated by verticillium wilt virus were measured in seedling stage, and totally 175 leaf point clouds were obtained. Totally 75 leaves were randomly selected to be cut off for manual validation, and the leaf area and perimeter were compared with manual measurements. The results showed that the mean absolute percentage error of leaf area and perimeter was 2.59% and 2.85%, respectively, the R2 values of leaf area and perimeter was 0.9973 and 0.9822, respectively. The results proved that the automatic measurement had a high accordance with manual measurements, which proved the high accuracy of this method. In addition, the left 100 leaves were divided into infected leaves and healthy leaves by manual observation, meanwhile the leaf traits were extracted with segmented point cloud data to calculate the P value by single factor analysis of variance. The measured P values were 0.099, 0.242, 0.346, 0.531, 0.002 and 0, respectively, and the results proved that the traits of leaf rolling degree, and leaf yellow ratio were able to distinguish the infected leaves from healthy leaves evidently. In conclusion, the study demonstrated an effective novel method for accurate and nondestructive measurement of cotton seedling leaf traits, which would be helpful for the cotton breeding, disease resistance research and functional gene mapping research. 