Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat

Author:

Zhu YuleiORCID,Sun GangORCID,Ding GuohuiORCID,Zhou JieORCID,Wen Mingxing,Jin ShichaoORCID,Zhao QiangORCID,Colmer JoshuaORCID,Ding YanfengORCID,Ober Eric S.ORCID,Zhou JiORCID

Abstract

AbstractPlant phenomics is widely recognised as a key area to bridge the gap between traits of agricultural importance and genomic information. A wide range of field-based phenotyping solutions have been developed, from aerial-based to ground-based fixed gantry platforms and handheld devices. Nevertheless, several disadvantages of these current systems have been identified by the research community concerning mobility, affordability, throughput, accuracy, scalability, as well as the ability to analyse big data collected. Here, we present a novel phenotyping solution that combines a commercial backpack LiDAR device and our graphical user interface (GUI) based software called CropQuant-3D, which has been applied to phenotyping of wheat and associated 3D trait analysis. To our knowledge, this is the first use of backpack LiDAR for field-based plant research, which can acquire millions of 3D points to represent spatial features of crops. A key feature of the innovation is the GUI software that can extract plot-based traits from large, complex point clouds with limited computing time and power. We describe how we combined backpack LiDAR and CropQuant-3D to accurately quantify crop height and complex 3D traits such as variation in canopy structure, which was not possible to measure through other approaches. Also, we demonstrate the methodological advance and biological relevance of our work in a case study that examines the response of wheat varieties to three different levels of nitrogen fertilisation in field experiments. The results indicate that the combined solution can differentiate significant genotype and treatment effects on key morphological traits, with strong correlations with conventional manual measurements. Hence, we believe that the combined solution presented here could consistently quantify key traits at a larger scale and more quickly than heretofore possible, indicating the system could be used as a reliable research tool in large-scale and multi-location field phenotyping for crop research and breeding activities. We exhibit the system’s capability in addressing challenges in mobility, throughput, and scalability, contributing to the resolution of the phenotyping bottleneck. Furthermore, with the fast maturity of LiDAR technologies, technical advances in image analysis, and open software solutions, it is likely that the solution presented here has the potential for further development in accuracy and affordability, helping us fully exploit available genomic resources.

Publisher

Cold Spring Harbor Laboratory

Reference121 articles.

1. AHDB (2015) Wheat Growth Guide. AHDB, Cereals and Oilseeds, Stoneleigh Park, Warwickshire

2. Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area;Comput Electron Agric,2016

3. Determining leaf area index and leafy tree roughness using terrestrial laser scanning;Water Resour Res,2010

4. Field high-throughput phenotyping: the new crop breeding frontier

5. Leaf area index estimation in vineyards using a ground-based LiDAR scanner;Precis Agric,2013

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3