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根室监测仪:一种用于表征根系生长的机器人平台及软件。

RhizoChamber-Monitor: a robotic platform and software enabling characterization of root growth.

作者信息

Wu Jie, Wu Qian, Pagès Loïc, Yuan Yeqing, Zhang Xiaolei, Du Mingwei, Tian Xiaoli, Li Zhaohu

机构信息

1State Key Laboratory of Plant Physiology and Biochemistry, Key Laboratory of Crop Cultivation and Farming System, Center of Crop Chemical Control, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.

3Present Address: Plant Phenomics Research Center, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.

出版信息

Plant Methods. 2018 Jun 7;14:44. doi: 10.1186/s13007-018-0316-5. eCollection 2018.

Abstract

BACKGROUND

In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development.

RESULTS

We describe a prototype robotic monitoring platform-the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software.

CONCLUSIONS

The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.

摘要

背景

为了有效确定作物生根模式的基因型差异,需要同时使用新型硬件和软件来表征根系发育动态。

结果

我们描述了一个用于自动分析植物根系生长模式的机器人监测平台原型——根室监测仪。根室监测仪包括一个自动成像系统,用于获取在定制根盒中布质基质上生长的根系的序列图像、一个自动灌溉系统和一个灵活的遮光装置。开发了一种定制的图像处理软件,用于从多株植物的时间序列图像中分析根系生长的时空动态。该软件可以量化根系的整体生长,并自动提取主根和单个侧根的详细生长特征(如长度和直径动态)。它还可以半自动识别侧根的局部生长特征(伪平均长度和伪最大长度)。使用两种棉花基因型对物理平台和分析软件进行了测试。

结论

硬件和软件的结合有望促进根系几何形状及其时空生长模式的量化,从而为高通量根系表型分析提供机会,以支持作物育种优化根系结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/5991437/c843e02cc852/13007_2018_316_Fig1_HTML.jpg

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