BACKGROUND AND AIMS High throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with UAV, and to compare the integrity of the established three-dimensional (3D) canopy by these two methods. METHODS Images were captured by a high-resolution digital RGB camera mounted on UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point cloud, ortho-mosaic and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. LAI was calculated by the 3D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. KEY RESULTS Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI ( slope=0.87, R2 = 0.67), compared with those of nadir photography( slope=0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7 - 3.1 times those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. CONCLUSIONS Plant height and LAI can be extracted automatically and efficiently by both photographing methods. Oblique photography can obtain intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3D profile of the plant canopy can be easily recognized by oblique photography.

中文翻译：

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使用无人机的倾斜和天底摄影估计玉米株高和叶面积指数动态

背景和目标 由于该领域的大规模实验，高通量表型分析是植物遗传学和育种的限制。无人机 (UAV) 可以帮助快速、无损、高效地提取植物表型特征。本研究的总体目的是通过无人机的最低点和倾斜摄影估计动态植物高度和叶面积指数（LAI），并比较这两种方法建立的三维（3D）冠层的完整性。方法采用无人机搭载的高分辨率数字RGB相机分五个阶段拍摄图像，采用天底和倾斜摄影，并通过Agisoft Metashape进行处理，生成点云、正射马赛克和数字表面模型。单个地块根据它们在实验设计布局中的位置进行分割。每个自交系的株高通过参考地面方法自动计算。LAI 是通过 3D 体素方法计算的。重建的冠层被切成不同的层，以比较从倾斜和最低点摄影中获得的叶面积密度。主要结果 在整个生长季节，最低点摄影、倾斜摄影和手动测量之间的植物高度具有良好的一致性。与最低点摄影（斜率=0.74，R2=0.56）相比，倾斜摄影估计的LAI与实测LAI的相关性更好（斜率=0.87，R2=0.67）。倾斜摄影获得的点云总数约为天底摄影的2.7-3.1倍。天底摄影计算的叶面积密度远小于斜摄影计算的叶面积密度，尤其是在植物基部附近。结论 两种拍照方法均可自动高效提取株高和LAI。倾斜摄影可以以较低的成本获得密集的点云和相对完整的冠层信息。植物冠层的重建 3D 轮廓可以通过倾斜摄影轻松识别。