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Automated Measurement of Plant Height of Wheat Genotypes Using a DSM Derived from UAV Imagery

机译：使用从无人机图像衍生的DSM自动测量小麦基因型的株高

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In this study, we have evaluated the use of unmanned air vehicle (UAV) photogrammetry for the monitoring of a wheat experiment under field conditions, filtered a digital surface model (DSM) to derive the wheat plant heights, and compared the results with the field measurements. The images were acquired with the use of a low-cost UAV Walkera QR350 and GoProHero3+ action camera in May 2015. In total, 477 images were acquired for quality assessment of the proposed method, and a reference dataset was collected with terrestrial fieldwork. For a comparison of field measurements with DSM-derived plant heights, the maximum calculated plant height in the plot was selected. The mean, median, and standard deviation were calculated as 4.66, 3.75, and 13.78 cm. Regarding the statistical t-test between the field measurements and plant heights from the DSM, the t-value was calculated as 1.82, and the p-value was 0.071. Because the t-value was larger than 0.50, the values between the traditional method and our approach were highly correlated.
机译：在这项研究中，我们评估了无人飞行器（UAV）摄影测量法在田间条件下监测小麦实验的效果，过滤了数字表面模型（DSM）以得出小麦株高，并将结果与​​田间比较测量。 2015年5月，使用低成本的无人机Walkera QR350和GoProHero3 +运动相机获取了图像。总共获取了477幅图像，用于评估该方法的质量，并通过地面野外工作收集了参考数据集。为了将田间测量结果与DSM得出的植物高度进行比较，选择了图中最大的计算植物高度。计算的平均值，中位数和标准偏差为4.66、3.75和13.78 cm。关于田间测量和来自DSM的植物高度之间的统计t检验，t值被计算为1.82，p值是0.071。由于t值大于0.50，因此传统方法与我们的方法之间的值高度相关。

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