Journal of Agricultural Science and Technology ›› 2023, Vol. 25 ›› Issue (8): 115-125.DOI: 10.13304/j.nykjdb.2022.0861


Organ Segmentation and Phenotypic Analysis of Soybean Plants Based on Three-dimensional Point Clouds

Yitong XIAO(), Shuai LIU, Chenlian HOU, Qi LIU, Fuzhong LI, Wuping ZHANG()   

  1. School of Software,Shanxi Agricultural University,Shanxi Taigu 030801,China
  • Received:2022-10-12 Accepted:2022-12-08 Online:2023-08-20 Published:2023-09-07
  • Contact: Wuping ZHANG


肖奕同(), 刘帅, 侯晨连, 刘琦, 李富忠, 张吴平()   

  1. 山西农业大学软件学院,山西 太谷 030801
  • 通讯作者: 张吴平
  • 作者简介:肖奕同
  • 基金资助:


In order to solve the problem of high-throughput phenotypic measurements caused by clustering and mutual shading of leaves of multi-branched crops such as soybean, a method of organ segmentation and phenotypic parameter measurement based on three-dimensional(3D)point clouds of the plant was proposed. Firstly, multi-view images of soybean plants were collected at the branching stage and used 3D reconstruction technology to obtain dense point clouds of plants, filtered point cloud noise and restored the actual scale. Further, the difference of normals algorithm, the improved regional growth algorithm and the point clouds curvature feature were used to segment the organs of the plant. Finally, the leaf area, leaf width, leaf length, leaf inclination angle and stem diameter were extracted by using the oriented bounding box, improved triangulation and the nearest neighbor algorithm. The results showed that the average segmentation rate of canopy leaf point clouds after organ segmentation was 84.24%, and the segmentation rate of single leaf point clouds was higher than 95.29%, and the measured values of phenotypic parameters had strong correlation with the manually measured values. The coefficients of determination of leaf area, leaf width, leaf length, leaf inclination and stem diameter measurements and manual measurements were 0.987 9, 0.961 3, 0.962 6, 0.931 1 and 0.963 4, respectively, with root mean square errors of 0.541 7 cm2, 0.141 2 cm, 0.175 5 cm, 3.279 6° and 0.047 5 cm. The proposed method had a good segmentation effect on plants with leaves adhering to each other and provided an effective solution for organ segmentation and phenotypic parameter measurement of multi-branched crops.

Key words: three-dimensional point clouds, organ segmentation, leaf segmentation, measurement of phenotypic parameters


为了解决大豆等多分枝作物叶片成簇、叶片相互遮挡带来的高通量表型测量困难问题,提出了基于植株三维点云的器官分割及表型参数测量方法。以分枝期大豆植株为研究对象,采集植株多视角图像,利用三维重建技术得到植株稠密点云、过滤点云噪声并还原实际尺度;以法线微分差异算法、改进的区域生长算法以及点云曲率特征实现植株各器官的分割;最后采用有向包围盒、改进的三角剖分法以及最邻近算法提取植株叶面积、叶宽、叶长、叶倾角和茎粗等表型参数。试验结果表明,器官分割后冠层叶片点云平均分割率为84.24%,单叶点云分割率均高于95.29%,表型参数测量值与人工实测值具有较强相关性,叶面积、叶宽、叶长、叶倾角和茎粗测量值与人工实测值的决定系数分别为0.987 9、0.961 3、0.962 6、0.931 1和0.963 4,均方根误差分别为0.541 7 cm2、0.141 2 cm、0.175 5 cm、3.279 6°和0.047 5 cm。提出的方法对叶片相互粘连的植株具有较好的分割效果,为多分枝作物的器官分割及表型参数测量提供了有效的解决方案。

关键词: 三维点云, 器官分割, 叶片分割, 表型参数测量

CLC Number: