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RhizoPot platform: A high-throughput in situ root phenotyping platform with integrated hardware and software

文献类型： 外文期刊

作者： Zhao, Hongjuan 1 ; Wang, Nan 1 ; Sun, Hongchun 1 ; Zhu, Lingxiao 1 ; Zhang, Ke 1 ; Zhang, Yongjiang 1 ; Zhu, Jijie 3 ; Li, Anchang 1 ; Bai, Zhiying 1 ; Liu, Xiaoqing 1 ; Dong, Hezhong 4 ; Liu, Liantao 1 ; Li, Cundong 1 ;

作者机构： 1.Hebei Agr Univ, Coll Agron, State Key Lab North China Crop Improvement & Regul, Key Lab Crop Growth Regulat Hebei Prov, Baoding, Peoples R China

2.Hebei Agr Univ, Coll Mech & Elect Engn, Baoding, Peoples R China

3.Hebei Acad Agr & Forestry Sci, Inst Cereal & Oil Crops, Shijiazhuang, Peoples R China

4.Shandong Acad Agr Sci, Cotton Res Ctr, Shandong Key Lab Cotton Culture & Physiol, Jinan, Peoples R China

关键词： rhizopot; high-throughput; growth; image acquisition; phenotyping; plant roots; root hair; root lifespan

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Quantitative analysis of root development is becoming a preferred option in assessing the function of hidden underground roots, especially in studying resistance to abiotic stresses. It can be enhanced by acquiring non-destructive phenotypic information on roots, such as rhizotrons. However, it is challenging to develop high-throughput phenotyping equipment for acquiring and analyzing in situ root images of root development. In this study, the RhizoPot platform, a high-throughput in situ root phenotyping platform integrating plant culture, automatic in situ root image acquisition, and image segmentation, was proposed for quantitative analysis of root development. Plants (1-5) were grown in each RhizoPot, and the growth time depended on the type of plant and the experimental requirements. For example, the growth time of cotton was about 110 days. The imaging control software (RhizoAuto) could automatically and non-destructively image the roots of RhizoPot-cultured plants based on the set time and resolution (50-4800 dpi) and obtain high-resolution (> 1200 dpi) images in batches. The improved DeepLabv3+ tool was used for batch processing of root images. The roots were automatically segmented and extracted from the background for analysis of information on radical features using conventional root software (WinRhizo and RhizoVision Explorer). Root morphology, root growth rate, and lifespan analysis were conducted using in situ root images and segmented images. The platform illustrated the dynamic response characteristics of root phenotypes in cotton. In conclusion, the RhizoPot platform has the characteristics of low cost, high-efficiency, and high-throughput, and thus it can effectively monitor the development of plant roots and realize the quantitative analysis of root phenotypes in situ.

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