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

Authors

Ronghao Wang, University of Nebraska - Lincoln
Yumuo Qiu, Iowa State University
Yuzhen Zhou, University of Nebraska - Lincoln
Zhikai Liang, University of Minnesota, Saint Paul
James Schnable, University of Nebraska-LincolnFollow

ORCID IDs

https://orcid.org/0000-0002-6441-7580

https://orcid.org/0000-0003-4846-1263

https://orcid.org/0000-0002-9963-8631

https://orcid.org/0000-0001-6739-5527

Document Type

Article

Date of this Version

2020

Citation

AAAS Plant Phenomics Volume 2020, Article ID 7481687, 8 pages https://doi.org/10.34133/2020/7481687

Comments

2020 Ronghao Wang et al.

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.