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Front. Plant Sci. | doi: 10.3389/fpls.2019.00554

Field-based High-throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated with a “Phenomobile”

 Quan Qiu1*,  Na Sun2, Yanjun Wang2,  Zhengqiang Fan3, Zhijun Meng1, Bin Li1 and Yue Cong1
  • 1Beijing Research Center for Intelligent Equipment for Agriculture, China
  • 2College of Mechanical and Electrical Engineering, Agricultural University of Hebei, China
  • 3College of Mechanical and Electronic Engineering, Northwest A&F University, China

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 ROS (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° 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 (ICP) 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.

Keywords: high-throughput phenotyping, 3D LIDAR, Field-based, mobile robot, Maize, point cloud

Received: 29 Sep 2018; Accepted: 11 Apr 2019.

Edited by:

Yanbo Huang, United States Department of Agriculture, United States

Reviewed by:

WEIWEI SUN, Ningbo University, China
Jianfeng Zhou, University of Missouri, United States  

Copyright: © 2019 Qiu, Sun, Wang, Fan, Meng, Li and Cong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Quan Qiu, Beijing Research Center for Intelligent Equipment for Agriculture, Beijing, Beijing Municipality, China,