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Field-Based High-Throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated With a "Phenomobile"

文献类型： 外文期刊

作者： Qiu, Quan 1 ; Sun, Na 3 ; Bai, He 4 ; Wang, Ning 2 ; Fan, Zhengqiang 5 ; Wang, Yanjun 3 ; Meng, Zhijun 1 ; Li, Bin 1 ; Cong, 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing, Peoples R China

2.Oklahoma State Univ, Dept Biosyst & Agr Engn, Stillwater, OK 74078 USA

3.Agr Univ Hebei, Coll Mech & Elect Engn, Baoding, Peoples R China

4.Oklahoma State Univ, Dept Mech & Aerosp Engn, Stillwater, OK 74078 USA

5.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling, Shaanxi, Peoples R China

关键词： high-throughput phenotyping; 3D LiDAR; mobile robot; maize; point cloud; field-based

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.753； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2019 年 10 卷

页码：

收录情况： SCI

摘要： With the rapid rising of global population, the demand for improving breeding techniques to greatly increase the worldwide crop production has become more and more urgent. Most researchers believe that the key to new breeding techniques lies in genetic improvement of crops, which leads to a large quantity of phenotyping spots. Unfortunately, current phenotyping solutions are not powerful enough to handle so many spots with satisfying speed and accuracy. As a result, high-throughput phenotyping is drawing more and more attention. In this paper, we propose a new field-based sensing solution to high-throughput phenotyping. We mount a LiDAR (Velodyne HDL64-S3) on a mobile robot, making the robot a "phenomobile." We develop software for data collection and analysis under Robotic Operating System using open source components and algorithm libraries. Different from conducting phenotyping observations with an in-row and one-by-one manner, our new solution allows the robot to move around the parcel to collect data. Thus, the 3D and 360 degrees view laser scanner can collect phenotyping data for a large plant group at the same time, instead of one by one. Furthermore, no touching interference from the robot would be imposed onto the crops. We conduct experiments for maize plant on two parcels. We implement point cloud merging with landmarks and Iterative Closest Points to cut down the time consumption. We then recognize and compute the morphological phenotyping parameters (row spacing and plant height) of maize plant using depth-band histograms and horizontal point density. We analyze the cloud registration and merging performances, the row spacing detection accuracy, and the single plant height computation accuracy. Experimental results verify the feasibility of the proposed solution.

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