Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud

TL;DR

This paper presents an automatic 3D point cloud segmentation method for maize shoots that accurately distinguishes stems and leaves, enabling precise phenotypic trait extraction and supporting maize research applications.

Contribution

The study introduces a novel three-step segmentation approach combining skeleton extraction, coarse, and fine segmentation, effectively handling closely wrapped new leaves in maize seedlings.

Findings

Segmentation accuracy with mean F1 score of 0.963

High correlation (R2 > 0.94) between extracted traits and ground truth

Method successfully segments both mature and wrapped new leaves

Abstract

Nowadays, there are many approaches to acquire three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from three-dimensional (3D) point clouds remains challenging, especially for new emerging leaves that are very close and wrapped together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three main steps: skeleton extraction, coarse segmentation based on the skeleton, fine segmentation based on stem-leaf classification. The segmentation method was tested on 30 maize seedlings and compared with manually obtained ground truth. The mean precision, mean recall, mean micro F1 score and mean over accuracy of our segmentation algorithm were 0.964, 0.966, 0.963 and 0.969. Using the segmentation results, two applications were also developed in this paper, including phenotypic trait extraction and skeleton optimization. Six phenotypic parameters can be accurately and automatically measured, including plant height, crown diameter, stem height and diameter, leaf width and length. Furthermore, the values of R2 for the six phenotypic traits were all above 0.94. The results indicated that the proposed algorithm could automatically and precisely segment not only the fully expanded leaves, but also the new leaves wrapped together and close together. The proposed approach may 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