Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
Norddeutsche Pflanzenzucht, Hohenlieth, Holtsee D-24363 Germany.
Plant Methods. 2015 Feb 25;11:9. doi: 10.1186/s13007-015-0048-8. eCollection 2015.
Field-based high throughput phenotyping is a bottleneck for crop breeding research. We present a novel method for repeated remote phenotyping of maize genotypes using the Zeppelin NT aircraft as an experimental sensor platform. The system has the advantage of a low altitude and cruising speed compared to many drones or airplanes, thus enhancing image resolution while reducing blurring effects. Additionally there was no restriction in sensor weight. Using the platform, red, green and blue colour space (RGB), normalized difference vegetation index (NDVI) and thermal images were acquired throughout the growing season and compared with traits measured on the ground. Ground control points were used to co-register the images and to overlay them with a plot map.
NDVI images were better suited than RGB images to segment plants from soil background leading to two separate traits: the canopy cover (CC) and its NDVI value (NDVIPlant). Remotely sensed CC correlated well with plant density, early vigour, leaf size, and radiation interception. NDVIPlant was less well related to ground truth data. However, it related well to the vigour rating, leaf area index (LAI) and leaf biomass around flowering and to very late senescence rating. Unexpectedly, NDVIPlant correlated negatively with chlorophyll meter measurements. This could be explained, at least partially, by methodical differences between the used devices and effects imposed by the population structure. Thermal images revealed information about the combination of radiation interception, early vigour, biomass, plant height and LAI. Based on repeatability values, we consider two row plots as best choice to balance between precision and available field space. However, for thermography, more than two rows improve the precision.
We made important steps towards automated processing of remotely sensed data, and demonstrated the value of several procedural steps, facilitating the application in plant genetics and breeding. Important developments are: the ability to monitor throughout the season, robust image segmentation and the identification of individual plots in images from different sensor types at different dates. Remaining bottlenecks are: sufficient ground resolution, particularly for thermal imaging, as well as a deeper understanding of the relatedness of remotely sensed data and basic crop characteristics.
基于田间的高通量表型分析是作物育种研究的一个瓶颈。我们提出了一种利用 Zeppelin NT 飞机作为实验传感器平台对玉米基因型进行重复远程表型分析的新方法。与许多无人机或飞机相比，该系统具有低空和巡航速度的优势，从而提高了图像分辨率，同时减少了模糊效应。此外，传感器的重量没有限制。使用该平台，在整个生长季节获取了红、绿、蓝色彩空间（RGB）、归一化差异植被指数（NDVI）和热图像，并与地面测量的性状进行了比较。地面控制点用于对图像进行配准，并将其与地块图叠加。
与 RGB 图像相比，NDVI 图像更适合于将植物与土壤背景分开，从而产生两个单独的性状：冠层覆盖度（CC）及其 NDVI 值（NDVIPlant）。远程感知的 CC 与植物密度、早期活力、叶片大小和辐射截获高度相关。NDVIPlant 与地面真值数据的相关性较差。然而，它与活力评分、开花期前后的叶面积指数（LAI）和叶生物量以及非常晚的衰老评分高度相关。出乎意料的是，NDVIPlant 与叶绿素计测量值呈负相关。这至少可以部分解释为所用设备之间的方法差异以及种群结构带来的影响。热图像揭示了辐射截获、早期活力、生物量、株高和 LAI 的综合信息。基于重复性值，我们认为双行地块是在精度和可用田间空间之间取得平衡的最佳选择。然而，对于热成像，增加超过两行可以提高精度。
我们朝着自动处理远程感测数据迈出了重要的一步，并展示了几个程序步骤的价值，这些步骤有助于在植物遗传学和育种中的应用。重要的发展包括：能够在整个季节进行监测、稳健的图像分割以及识别来自不同传感器类型和不同日期的图像中的单个地块的能力。剩余的瓶颈包括：足够的地面分辨率，特别是对于热成像，以及更深入地了解远程感测数据与基本作物特性之间的关系。
