Growth dynamics for plant high-throughput phenotyping studies using hierarchical functional data analysis

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. 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 dynamics are revealed in the application to a modern plant phenotyping study.

Speaker Bio: Yuhang Xu is an assistant professor in statistics in the Department of Applied Statistics and Operations Research at Bowling Green State University. He received his Ph.D. in Statistics from Iowa State University in 2016. After graduation, he worked as an assistant professor in the Department of Statistics at the University of Nebraska-Lincoln from 2016 to 2019. His research interests include functional data analysis, survival analysis, measurement error, and interdisciplinary research in business, plant science, chemistry, etc.