scholarly journals Crop Root Behavior Coordinates Phosphorus Status and Neighbors: From Field Studies to Three-Dimensional in Situ Reconstruction of Root System Architecture

2011 ◽  
Vol 155 (3) ◽  
pp. 1277-1285 ◽  
Author(s):  
Suqin Fang ◽  
Xiang Gao ◽  
Yan Deng ◽  
Xinping Chen ◽  
Hong Liao
2014 ◽  
Vol 13 (8) ◽  
pp. vzj2014.03.0024 ◽  
Author(s):  
Nicolai Koebernick ◽  
Ulrich Weller ◽  
Katrin Huber ◽  
Steffen Schlüter ◽  
Hans-Jörg Vogel ◽  
...  

2014 ◽  
Vol 383 (1-2) ◽  
pp. 155-172 ◽  
Author(s):  
Yuan Wu ◽  
Li Guo ◽  
Xihong Cui ◽  
Jin Chen ◽  
Xin Cao ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shota Teramoto ◽  
Takanari Tanabata ◽  
Yusaku Uga

Abstract Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.


Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shota Teramoto ◽  
Satoko Takayasu ◽  
Yuka Kitomi ◽  
Yumiko Arai-Sanoh ◽  
Takanari Tanabata ◽  
...  

2019 ◽  
Vol 441 (1-2) ◽  
pp. 33-48 ◽  
Author(s):  
Yilin Fang ◽  
Steven B. Yabusaki ◽  
Amir H. Ahkami ◽  
Xingyuan Chen ◽  
Timothy D. Scheibe

2018 ◽  
Vol 121 (5) ◽  
pp. 1089-1104 ◽  
Author(s):  
Jean-François Barczi ◽  
Hervé Rey ◽  
Sébastien Griffon ◽  
Christophe Jourdan

2013 ◽  
Vol 12 (1) ◽  
pp. vzj2012.0019 ◽  
Author(s):  
Laura Stingaciu ◽  
Hannes Schulz ◽  
Andreas Pohlmeier ◽  
Sven Behnke ◽  
Herwig Zilken ◽  
...  

2020 ◽  
Vol 11 ◽  
Author(s):  
Waldiodio Seck ◽  
Davoud Torkamaneh ◽  
François Belzile

Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.


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