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Abstract. Three-dimensional (3D) point cloud based plant phenotyping facilitates the extraction of the structural information of phenotypic traits and reduces the effects of occlusion. In this study, we describe a cotton plant architectural component segmentation and phenotypic trait extraction method. Point cloud data of cotton plants were collected using a terrestrial LiDAR. Individual branches were segmented using a cylinder-fitting method. Next, plant architectural phenotypic traits were extracted at both plant and branch levels, including plant volume, branch length, diameter, volume, and branch angle. Two cotton plants with different shapes were used to demonstrate the performance of the proposed method. Results demonstrated that the method could process plants with not only an upright main stem but also a lodged main stem and the extracted phenotypic traits were reasonable. The proposed method could provide useful information and improve efficiency for plant breeding studies.