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Advancing structural-functional modelling of root growth and root-soilinteractions based on automatic reconstruction of root systems fromMRI

基于 MRI 根系自动重建，推进根系生长和根系-土壤相互作用的结构功能建模

基本信息

批准号：
274830790
负责人：
Professor Dr. Sven Behnke
金额：
--
依托单位：
Informatik 6: Intelligente Systeme und Robotik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/274830790?language=en
关键词：
Advancing structural functional modelling root

项目摘要

Root system architecture and root uptake properties are critically important for soil exploration as roots are the major sites of soil-plant interactions. Crop breeding and management have increased interest in below-ground processes and root traits that may lead to more sustainable agricultural practices through reduced input of water and fertilisers. MRI is a non-invasive imaging method used to obtain both structural and functional information about the root-soil system. In combination with mathematical modelling, this will enhance our understanding of root-soil interactions. New techniques for automatic root segmentation and reconstruction of root systems are required to efficiently extract structural information about the root system from the vast amount of information contained in those volumetric measurements. In particular the connectivity of the root segments is important for modelling water flow in the root-soil system. In this project, we will simultaneously monitor root system architecture of soil-grown lupine plants and soil moisture at different times using MRI. With the help of manually annotated root systems, a method for learning fully automated segmentation of roots will be developed. {\textmu}CT images of selected samples will be used for comparison and evaluation of the new segmentation algorithm for root systems from MRI images. From the reconstructed roots, important root architectural features, such as basal and apical zone, internodal distance, curvature, and branching angles will be derived and used to refine the parameters of a root growth model, e.g. by improving proposal tropism angular distributions, branching angle, and axial growth parameters. Feedbacks between root system development and soil moisture conditions under water-sufficient and water-limited conditions will be evaluated. These findings will be used to advance mathematical models of water uptake by plant roots. Explicit 3D simulations of water flow will be performed on reconstructed root architectures using Comsol Multiphysics and evaluated based on measured soil water contents. This will then serve as a benchmark for testing different upscaling methods that result in an effective sink term for root water uptake from soil that can be incorporated into water flow models such as R-SWMS. Main results of this project will be a fully automated root system reconstruction algorithm and, based on this result, an advanced process understanding and mathematical model of root-water dynamics.

根系是土壤-植物相互作用的主要场所，根系构型和根系吸收特性对于土壤勘探至关重要。作物育种和管理增加了人们对地下过程和根系特征的兴趣，这些过程和特征可能通过减少水和肥料的投入而导致更可持续的农业实践。MRI是一种非侵入性成像方法，用于获得有关根-土壤系统的结构和功能信息。结合数学建模，这将提高我们对根-土相互作用的理解。需要用于自动根分割和重建根系的新技术来有效地从这些体积测量中包含的大量信息中提取关于根系的结构信息。特别是根段的连通性是重要的模拟水流在根-土壤系统。在这个项目中，我们将同时监测根系结构的土壤生长羽扇豆植物和土壤水分在不同的时间使用MRI。在人工标注的根系的帮助下，将开发一种用于学习根的全自动分割的方法。所选样本的CT图像将用于比较和评价MRI图像中根系的新分割算法。从重建的根，重要的根结构特征，如基部和顶端区，节间距离，曲率，和分支角度将被导出，并用于细化根生长模型的参数，例如通过改善建议取向角分布，分支角度，和轴向生长参数。将评估在水分充足和水分有限条件下根系发育和土壤水分条件之间的反馈。这些发现将用于推进植物根系吸水的数学模型。将使用Comsol Multiphysics对重建的根系结构进行显式三维水流模拟，并根据测量的土壤含水量进行评估。然后，这将作为一个基准测试不同的放大方法，导致一个有效的汇从土壤中的根系吸水，可以纳入水流模型，如R-SWMS。该项目的主要成果将是一个全自动的根系重建算法，并在此基础上，一个先进的过程理解和根水动力学的数学模型。