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一种用于图像处理和功能生长曲线分析的高通量表型分析流程

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

作者信息

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

机构信息

Department of Statistics, University of Nebraska-Lincoln, Lincoln 68503, USA.

Department of Statistics, Iowa State University, Ames 50011, USA.

出版信息

Plant Phenomics. 2020 Jul 14;2020:7481687. doi: 10.34133/2020/7481687. eCollection 2020.

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.

摘要

高通量表型分析系统在植物科学研究中越来越受欢迎。此类系统的数据分析通常包括两个步骤:通过图像处理提取植物特征以及对提取的特征进行统计分析。当前的方法是在不同平台上执行这两个步骤。我们在R语言中开发了“implant”软件包,用于稳健的特征提取和功能数据分析。对于图像处理,“implant”软件包提供了包括阈值处理、隐马尔可夫随机场模型和形态学操作在内的方法。对于统计分析,该软件包可以生成具有植物生长置信区域的非参数曲线拟合。还提供了一个功能方差分析模型,用于测试处理和基因型对植物生长动态的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5235/7706310/ba12dad9cf70/PLANTPHENOMICS2020-7481687.001.jpg

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