Automatic stem-leaf segmentation of maize shoots using three-dimensional point cloud

Abstract

The application of 3D point cloud data in maize research is increasingly extensive. Currently, there are many approaches to acquiring three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from 3D point clouds remains challenging, especially for new emerging leaves that are wrapped very closely together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three steps: skeleton extraction, coarse segmentation based on the skeleton, and fine segmentation based on stem-leaf classification. The segmentation method was tested on 75 maize seedlings and compared with the manually obtained ground truth. The mean precision, mean recall, mean micro F1 score, and mean overall accuracy of our segmentation algorithm were 0.944, 0.956, 0.950 and 0.953, respectively. Using the segmentation results, two applications were also developed in this study, namely, phenotypic trait extraction and skeleton optimization. Six phenotypic parameters, namely, plant height, crown diameter, stem height and diameter, leaf width, and length, can be accurately and automatically measured. Furthermore, the values of R2 for the six phenotypic traits were all above 0.92. We also propose a skeleton optimization method that can extract the skeletons of the upper leaves completely and clearly. The results indicate that the proposed algorithm can automatically and precisely segment not only the fully expanded leaves but also the new leaves wrapped closely together. The proposed approach can play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, and dynamic growth animation. We released the source code and test data at the web site https://github.com/syau-miao/seg4maize.git.