Lu Hang, Tang Lie, Whitham Steven A, Mei Yu
Department of Agricultural and Biosystems Engineering, Iowa State University, 2346 Elings Hall, Ames, IA 50011, USA.
Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA.
Sensors (Basel). 2017 Sep 12;17(9):2082. doi: 10.3390/s17092082.
Crop breeding plays an important role in modern agriculture, improving plant performance, and increasing yield. Identifying the genes that are responsible for beneficial traits greatly facilitates plant breeding efforts for increasing crop production. However, associating genes and their functions with agronomic traits requires researchers to observe, measure, record, and analyze phenotypes of large numbers of plants, a repetitive and error-prone job if performed manually. An automated seedling phenotyping system aimed at replacing manual measurement, reducing sampling time, and increasing the allowable work time is thus highly valuable. Toward this goal, we developed an automated corn seedling phenotyping platform based on a time-of-flight of light (ToF) camera and an industrial robot arm. A ToF camera is mounted on the end effector of the robot arm. The arm positions the ToF camera at different viewpoints for acquiring 3D point cloud data. A camera-to-arm transformation matrix was calculated using a hand-eye calibration procedure and applied to transfer different viewpoints into an arm-based coordinate frame. Point cloud data filters were developed to remove the noise in the background and in the merged seedling point clouds. A 3D-to-2D projection and an -axis pixel density distribution method were used to segment the stem and leaves. Finally, separated leaves were fitted with 3D curves for morphological traits characterization. This platform was tested on a sample of 60 corn plants at their early growth stages with between two to five leaves. The error ratios of the stem height and leave length measurements are 13.7% and 13.1%, respectively, demonstrating the feasibility of this robotic system for automated corn seedling phenotyping.
作物育种在现代农业中发挥着重要作用，可改善植物性能并提高产量。识别负责有益性状的基因极大地促进了旨在提高作物产量的植物育种工作。然而，将基因及其功能与农艺性状相关联需要研究人员观察、测量、记录和分析大量植物的表型，如果手动执行，这是一项重复性且容易出错的工作。因此，一个旨在取代人工测量、减少采样时间并增加可工作时间的自动化幼苗表型分析系统具有很高的价值。为了实现这一目标，我们基于飞行时间（ToF）相机和工业机器人手臂开发了一个自动化玉米幼苗表型分析平台。一个ToF相机安装在机器人手臂的末端执行器上。该手臂将ToF相机定位在不同的视点以获取三维点云数据。使用手眼校准程序计算相机到手臂的变换矩阵，并将其应用于将不同的视点转换到基于手臂的坐标系中。开发了点云数据滤波器以去除背景和合并的幼苗点云中的噪声。使用三维到二维投影和x轴像素密度分布方法来分割茎和叶。最后，用三维曲线拟合分离的叶子以表征形态特征。该平台在60株处于两到五叶早期生长阶段的玉米植株样本上进行了测试。茎高和叶长测量的误差率分别为13.7%和13.1%，证明了该机器人系统用于自动化玉米幼苗表型分析的可行性。
