Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

中文翻译：

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高通量植物表型系统生成的表型数据的发育标准化。

播种时间通常用作高通量植物表型 (HTPP) 系统中拟南芥 (Arabidopsis) 实验的时间参考。这依赖于发芽和幼苗建立在整个种群中是一致的假设。然而，即使在统一的环境条件下，个体种子也具有不同的发育轨迹。这导致定量表型方法的差异增加。我们开发了植物发育的数字调整 (DAPD) 标准化方法。它通过参考早期发展阶段并以自动化方式标准化时间序列 HTPP 测量。每个测量系列的时间线被转移到一个参考时间。归一化由时间序列测量的多个时间点的互相关确定，这可能包括玫瑰花结面积、叶子大小和数量。DAPD 方法通过减少时间序列中数量性状的统计离散度来提高表型测量的准确性。我们应用 DAPD 来评估拟南芥植物的相对生长速率，并证明它提高了测量的均匀性，允许在个体之间进行更多信息比较。与播种时间标准化相比，DAPD 的应用将表型测量的方差降低了 2.5 倍。与应用于非标准化数据集的任何其他集中趋势技术相比，DAPD 方法还识别出更多的异常值。DAPD 是控制植物表型数据集中发育时间差异的有效方法。原则，