田间作物群体三维点云柱体空间分割方法  

Cylinder space segmentation method for field crop population using 3D point cloud

作　　者：林承达[1] 韩晶 谢良毅 胡方正 Lin Chengda;Han Jing;Xie Liangyi;Hu Fangzheng(College of Resource and Environment,Huazhong Agricultural University,Wuhan 430070,China)

机构地区：[1]华中农业大学资源与环境学院,武汉430070

出　　处：《农业工程学报》2021年第7期175-182,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(41301522);中央高校基本科研业务费专项(2662018JC054);湖北省自然科学基金项目(2014CFB940)。

摘　　要：农田作物群体表型信息对于研究作物内部基因改变和培育优良品种具有重要意义。为实现田间作物群体点云数据中单个植株对象的完整提取与分割,以便于更高效地完成作物个体表型参数的自动测量,该研究提出一种田间作物柱体空间聚类分割方法。利用三维激光扫描仪获取田间油菜、玉米和棉花的三维点云数据,基于HSI(Hue-Saturation-Intensity,色调、饱和度、亮度)颜色模型进行作物群体目标提取,采用直通滤波方法获取作物茎秆点云,基于茎秆点云数据使用欧氏距离聚类分割算法提取每个植株的聚类中心点,并以聚类中心点建立柱体空间模型,使用该模型分割得到田间作物每个单体植株的点云数据。试验结果表明,该研究的方法对油菜、玉米和棉花3种作物的分割准确率分别为90.12%、96.63%和100%,与欧氏距离聚类分割结果相比,准确率分别提高了36.42,61.80和82.69个百分点,算法耗时分别缩短为后者的9.98%,16.40%和9.04%,与区域增长算法分割结果相比,该研究的方法可用于不同类型农作物,适用性更强,能够实现农田中较稠密作物植株的分割。该研究的方法能够实现农田尺度下单个植株的完整提取与分割,具有较高的适用性,可为精确测量作物个体表型信息提供参考。A new phenotype of crop population depends mainly on the internal genetic change of plants with environment,thereby determining new varieties of crops in farmland.A three-dimensional(3D)laser scanning technology can provide a rapid acquisition for the accurate phenotypic data of crops,compared with some traditional time-consuming and destructive measurements.However,field high-throughput phenotypic acquisition is still a major bottleneck limiting crop improvement and precision agriculture.It is also necessary to automatically acquire phenotypic traits throughout the growth cycle of crops and further to obtain target parameters with high accuracy.In this study,a cylinder space clustering segmentation was proposed for a highly efficient extraction on complete phenotypic parameters of a single plant in field crop population using a 3D point cloud.Field experiments were carried out at the Huazhong Agricultural University in Wuhan City,Hubei Province of China in 2019.Flowering rapeseed,seedling corn,and flowering cotton were selected as the research objects.The experimental procedure was:1)A 3D laser scanner(FARO FocusS SeriesS 70)was used to collect high-precision point cloud data of field corn,rapeseed and cotton.Multiple sites were set around the experimental field for high accuracy information about the target.The measuring sites of rapeseed field were laid in the four corners and the middle of the long side of a sample plot.Four corners of a sample plot were selected to measure in corn and cotton field.Two groups of point cloud data were collected at different heights in the same measuring site.Each position was scanned once,and each scanning took 10 min.At least 3 target balls were placed in the test area as the registration basis,thereby preparing for the registration of point cloud data collected by subsequent test stations.2)The crop target was then extracted from the massive point cloud,including registration,denoising,data extraction,and simplification.The point cloud registration was completed using a targ

关 键 词：作物 激光 三维点云 柱体空间模型 分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]