Research Article | Open Access

Volume 2020 |Article ID 7481687 | https://doi.org/10.34133/2020/7481687

A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

Ronghao WangiD ,¹ Yumou Qiu iD ,² Yuzhen Zhou,¹ Zhikai LiangiD ,³ and James C. SchnableiD ⁴

¹Department of Statistics, University of Nebraska-Lincoln, Lincoln 68503, USA
²Department of Statistics, Iowa State University, Ames 50011, USA
³Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
⁴Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln 68503, USA

Abstract

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.