论文标题
从三维点云中的玉米芽的茎叶分割和表型性状提取
Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud
论文作者
论文摘要
如今，有许多方法可以获取玉米植物的三维（3D）点云。但是，从三维（3D）点云中自动对玉米芽的茎叶片仍然具有挑战性，尤其是对于在幼苗阶段非常接近并包裹在一起的新的新兴叶子。为了解决此问题，我们提出了一种由三个主要步骤组成的自动分割方法：骨架提取，基于骨架的粗分割，基于茎叶分类的细分段。分割方法在30个玉米幼苗上进行了测试，并与手动获得的地面真相进行了比较。分割算法的平均精度，平均召回，平均微F1得分和平均值超过准确性为0.964、0.966、0.963和0.969。使用分割结果，本文还开发了两个应用，包括表型性状提取和骨骼优化。可以准确，自动测量六个表型参数，包括植物高度，冠直径，茎高和直径，叶子宽度和长度。此外，六个表型性状的R2值均高于0.94。结果表明，所提出的算法不仅可以自动，精确地分段，不仅片段膨胀的叶子，还可以将新的叶子包裹在一起并结合在一起。所提出的方法可能在进一步的玉米研究和应用中起重要作用，例如基因型对表型研究，几何重建和动态增长动画。我们在网站上发布了源代码和测试数据https://github.com/syau-miao/seg4maize.git
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
