In modern high-throughput plant phenotyping, images of plants of different genotypes are repeatedly taken throughout the growing season, and phenotypic traits of plants (e.g., plant height) are extracted through image processing. It is of interest to recover whole trait trajectories and their derivatives at both genotype and plant levels based on observations made at irregular discrete time points. We propose to model trait trajectories using hierarchical functional principal component analysis (HFPCA) and show that the problem of recovering derivatives of the trajectories is reduced to estimating derivatives of eigenfunctions, which is solved by differentiating eigenequations. Based on HFPCA, we also propose a new measure for the broad-sense heritability by allowing it to vary over time during plant growth. Simulation studies show that the proposed procedure performs better than its competitors in terms of recovering both trait trajectories and their derivatives. Interesting characteristics of plant growth and heritability dynamics are revealed in the application to a modern plant phenotyping study.

中文翻译：

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使用分层功能数据分析进行植物高通量表型研究的生长动态和遗传力

在现代高通量植物表型分析中，在整个生长季节重复拍摄不同基因型植物的图像，并通过图像处理提取植物的表型性状（例如株高）。基于在不规则离散时间点进行的观察，在基因型和植物水平上恢复整个性状轨迹及其衍生物是有意义的。我们建议使用层次函数主成分分析（HFPCA）对性状轨迹进行建模，并表明恢复轨迹导数的问题被简化为估计本征函数的导数，这可以通过微分特征方程来解决。基于 HFPCA，我们还提出了一种新的广义遗传力测量方法，允许其在植物生长过程中随时间变化。模拟研究表明，所提出的程序在恢复性状轨迹及其导数方面比其竞争对手表现更好。在现代植物表型研究的应用中揭示了植物生长和遗传力动态的有趣特征。