Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low‐cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV‐based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV‐based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.

中文翻译：

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基于无人机的成像平台，用于监测玉米整个发育过程中的生长情况。


以高时间分辨率收集的植物高度 (PH) 数据可以深入了解基因型和环境变化如何影响植物生长。然而，为了提高 PH 数据收集的时间分辨率，需要比现有方法更稳健、更快速、更低成本的方法来评估田间图。由于其低成本和高功能，无人机 (UAV) 提供了一种在整个开发的各个阶段收集高度的有效方法。我们开发了一种利用运动算法结构从 RGB 无人机图像中收集 PH 的程序，并使用该平台来表征产量试验，该试验包括在两个日期和三种种植密度下重复种植的 24 个玉米杂交种。 PH 数据是通过每周无人机飞行和手动测量来收集的。基于无人机和手动获取的 PH 测量值的比较揭示了测量 PH 的误差源，并用于开发一个强大的管道来生成基于无人机的 PH 估计值。该管道用于记录基因型和种植日期之间生长速率的差异。我们的结果还表明，在发育早期的多个时间点收集的 PH 测量值产生的生长率可有助于改善对季节结束时 PH 的预测。该方法提供了一种低成本、高通量的方法，用于评估植物生长对环境刺激的响应，该方法可以在育种计划的规模上实施。