Li Yinglun, Wen Weiliang, Fan Jiangchuan, Gou Wenbo, Gu Shenghao, Lu Xianju, Yu Zetao, Wang Xiaodong, Guo Xinyu
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
Plant Phenomics. 2023 Apr 20;5:0043. doi: 10.34133/plantphenomics.0043. eCollection 2023.
The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements ( = 0.98), and the accuracy was higher than only using one source point cloud data ( = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.
能够在三维水平上获取植物群体高通量和时间序列表型的田间表型分析平台对于植物育种和管理至关重要。然而，点云数据的对齐以及植物群体准确表型性状的提取具有一定难度。在本研究中，利用基于田间轨道的带有光探测与测距（LiDAR）的表型分析平台以及RGB（红、绿、蓝）相机，收集了田间玉米群体的高通量、时间序列原始数据。通过直接线性变换算法对齐正射校正图像和LiDAR点云。在此基础上，通过时间序列图像引导进一步配准时间序列点云。然后使用布料模拟滤波算法去除地面点。通过快速位移和区域生长算法从玉米群体中分割出单株植物和植物器官。利用多源融合数据获得的13个玉米品种的株高与人工测量高度高度相关（ = 0.98），且精度高于仅使用单一源点云数据（ = 0.93）。这表明多源数据融合能够有效提高时间序列表型提取的准确性，基于轨道的田间表型分析平台可以成为在单株植物和器官尺度上进行植物生长动态表型观测的实用工具。
