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

Ronghao Wang,Yuzhen Zhou,James C Schnable,Zhikai Liang,Yumou Qiu

doi:10.34133/2020/7481687

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.

Highlights

High-throughput phenotyping is a newly emerging technique in the plant science research
We illustrate the implementation of our “implant” package by a maize experiment conducted at the University of Nebraska-Lincoln (UNL) Greenhouse Innovation Center
We developed a comprehensive pipeline for analyzing high-throughput plant phenotyping data that includes RGB image preprocessing, plant feature extraction, and functional data modeling and inference for growth curve dynamics

Summary

Introduction

High-throughput phenotyping is a newly emerging technique in the plant science research. Many automated systems have been constructed both in the greenhouse and field to study plant features [1,2,3]). One of the main innovations is to use automated cameras to take raw images for plants. Several types of high-resolution images, including RGB, infrared, flourescence, and hyperspectral, are recorded for a large number of plants at designed time points. We are able to process and extract useful phenotypical features, such as plant height, width, and size. Compared to the traditional methods, the high-throughput system is able to provide the plant features of interest in a more efficient, accurate and nondestructive way

Methods

Results

Conclusion