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首页> 外文期刊>Functional Plant Biology >GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons.
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GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons.

机译:GROWSCREEN-Rhizo是一种新颖的表型分型机器人,可以同时测量在充满土壤的根际生物中生长的植物的根和茎生长。

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摘要

Root systems play an essential role in ensuring plant productivity. Experiments conducted in controlled environments and simulation models suggest that root geometry and responses of root architecture to environmental factors should be studied as a priority. However, compared with aboveground plant organs, roots are not easily accessible by non-invasive analyses and field research is still based almost completely on manual, destructive methods. Contributing to reducing the gap between laboratory and field experiments, we present a novel phenotyping system (GROWSCREEN-Rhizo), which is capable of automatically imaging roots and shoots of plants grown in soil-filled rhizotrons (up to a volume of ~18L) with a throughput of 60 rhizotrons per hour. Analysis of plants grown in this setup is restricted to a certain plant size (up to a shoot height of 80cm and root-system depth of 90cm). We performed validation experiments using six different species and for barley and maize, we studied the effect of moderate soil compaction, which is a relevant factor in the field. First, we found that the portion of root systems that is visible through the rhizotrons' transparent plate is representative of the total root system. The percentage of visible roots decreases with increasing average root diameter of the plant species studied and depends, to some extent, on environmental conditions. Second, we could measure relatively minor changes in root-system architecture induced by a moderate increase in soil compaction. Taken together, these findings demonstrate the good potential of this methodology to characterise root geometry and temporal growth responses with relatively high spatial accuracy and resolution for both monocotyledonous and dicotyledonous species. Our prototype will allow the design of high-throughput screening methodologies simulating environmental scenarios that are relevant in the field and will support breeding efforts towards improved resource use efficiency and stability of crop yields.
机译:根系在确保工厂生产力方面发挥着至关重要的作用。在受控环境和模拟模型中进行的实验表明,应优先研究根的几何形状和根对环境因素的响应。但是,与地上的植物器官相比,通过非侵入式分析不容易找到根部,而田间研究仍几乎完全基于人工的破坏性方法。为了缩小实验室和野外实验之间的距离,我们提出了一种新型的表型分型系统(GROWSCREEN-Rhizo),该系统能够自动成像在充满土壤的根际(最大约18L)中生长的植物的根和芽。每小时可生产60个根管。在这种设置下生长的植物的分析仅限于一定的植物大小(最高芽高为80cm,根系深度为90cm)。我们使用六个不同的物种进行了验证实验,对于大麦和玉米,我们研究了土壤适度压实的效果,这是该领域的一个相关因素。首先,我们发现通过根茎管的透明板可见的根系部分代表了整个根系。可见根的百分比随着所研究植物物种的平均根直径的增加而降低,并且在一定程度上取决于环境条件。其次,我们可以测量土壤压实度适度增加引起的根系结构相对较小的变化。综上所述,这些发现表明,该方法具有很高的潜力,可以以相对较高的空间精度和分辨率对单子叶和双子叶物种表征根部几何形状和时间生长响应。我们的原型将允许设计高通量筛选方法,以模拟与现场相关的环境情景,并将支持育种工作,以提高资源利用效率和作物产量的稳定性。

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