Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat
Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat
复制标题
DOI：
10.1101/2021.05.19.444842
复制
发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Yulei Zhu;Gang Sun;Guohui Ding;Jie Zhou;Mingxing Wen;Shichao Jin;Qiang Zhao;Joshua Colmer;Yanfeng Ding;E. Ober;Ji Zhou
中科院分区：
文献类型：
--
作者：
Yulei Zhu;Gang Sun;Guohui Ding;Jie Zhou;Mingxing Wen;Shichao Jin;Qiang Zhao;Joshua Colmer;Yanfeng Ding;E. Ober;Ji Zhou
植物表型组学被广泛认为是弥合农业重要性状和基因组信息之间差距的关键领域。已经开发了从空中到地面固定龙门平台和手持设备的广泛的基于现场的表型解决方案。然而，研究界发现，目前这些系统在机动性、可负担性、吞吐量、准确性、可扩展性以及分析收集的大数据的能力方面存在一些不足之处。在这里，我们提出了一种新的表型解决方案，它结合了商业背包式LiDAR设备和我们基于图形用户界面(GUI)的软件CropQuant-3D，该软件已应用于小麦的表型鉴定和相关的3D性状分析。据我们所知，这是首次将背包式LiDAR用于田间植物研究，它可以获取数百万个3D点来表示作物的空间特征。这项创新的一个关键功能是图形用户界面软件，它可以在有限的计算时间和能力下从大型、复杂的点云中提取基于图形的特征。我们描述了我们如何结合Backpack LiDAR和CropQuant-3D来精确量化作物高度和复杂的3D特征，如冠层结构的变化，这是其他方法无法测量的。此外，我们在一个案例研究中展示了我们工作的方法论进步和生物学相关性，该研究在田间试验中检查了小麦品种对三种不同氮肥水平的反应。结果表明，该组合方案能在关键形态性状上区分显著的基因型和处理效应，与传统的人工测量具有很强的相关性。因此，我们相信，本文提出的组合解决方案可以在更大的范围内和更快地一致地量化关键性状，表明该系统可以作为一种可靠的研究工具，用于作物研究和育种活动的大规模和多地点田间表型鉴定。我们展示了该系统在解决移动性、吞吐量和可扩展性方面的挑战的能力，有助于解决表型瓶颈。此外，随着LiDAR技术的快速成熟、图像分析技术的进步和开放的软件解决方案，本文提出的解决方案很可能在准确性和可负担性方面具有进一步发展的潜力，帮助我们充分利用现有的基因组资源。
Plant 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.