Analysis of root growth from a phenotyping data set using a density-based model

Abstract

Major research efforts are targeting the improved performance of root systems for more efficient use of water and nutrients by crops. However, characterizing root system architecture (RSA) is challenging, because roots are difficult objects to observe and analyse. A model-based analysis of RSA traits from phenotyping image data is presented. The model can successfully back-calculate growth parameters without the need to measure individual roots. The mathematical model uses partial differential equations to describe root system development. Methods based on kernel estimators were used to quantify root density distributions from experimental image data, and different optimization approaches to parameterize the model were tested. The model was tested on root images of a set of 89 Brassica rapa L. individuals of the same genotype grown for 14 d after sowing on blue filter paper. Optimized root growth parameters enabled the final (modelled) length of the main root axes to be matched within 1% of their mean values observed in experiments. Parameterized values for elongation rates were within ±4% of the values measured directly on images. Future work should investigate the time dependency of growth parameters using time-lapse image data. The approach is a potentially powerful quantitative technique for identifying crop genotypes with more efficient root systems, using (even incomplete) data from high-throughput phenotyping systems.

Dealing with multi‐source and multi‐scale information in plant phenomics: the ontology‐driven Phenotyping Hybrid Information System

“…Whereas it is accepted that measuring yield in controlled conditions is most often nonrelevant (Poorter et al, 2016), the high-throughput measurement of physiological variables in the field is often impossible; for example, the precise measurement of water or nutrient fluxes through the plant, or of architectural features of root or shoot systems. Such measurements are possible in controlled conditions, opening the way to the dissection of the genetic architecture of physiological traits (Mairhofer et al, 2013;Cabrera-Bosquet et al, 2016;Coupel-Ledru et al, 2016;Kalogiros et al, 2016;Alvarez Prado et al, 2018). Combining data in field and controlled conditions is possible, and provides valuable information for analysing and predicting the genotype 9 environment interaction of both traits and yields (Reymond et al, 2003;Lacube et al, 2017;Tardieu et al, 2018).…”

A new method for the rapid characterization of root growth and distribution using digital image correlation

“…which sample a small fraction of the roots (Wasson et al, 2016); (3) use of a soil substitute for growth, typically transparent, that allows for in situ root observationexamples include transparent soil (Downie et al, 2012) and agar/agar-like systems (Clark et al, 2011); (4) noninvasive imaging of root systems through a transparent surface using a standard camera (Belter & Cahill, 2015), a minirhizotron (McNickle & Cahill, 2009;Karst et al, 2012) or a scanner (Adu et al, 2014); and (5) noninvasive imaging capable of soil surface penetration, such as magnetic resonance imaging (MRI) (Metzner et al, 2014) and computed tomography (CT) scanning (Lontoc-Roy et al, 2005;Flavel et al, 2012). Development in computational techniques aimed at maximizing and expediting the extraction of imaging information has paralleled the development of these experimental methods (Cai et al, 2015;Hatzig et al, 2015;Kalogiros et al, 2016).…”

High-resolution 4D spatiotemporal analysis reveals the contributions of local growth dynamics to contrasting maize root architectures

“…Intensive research has yielded detailed molecular and cellular mechanisms of how single roots grow at the local scale (reviewed in: (12,13)) on one hand, and the identification of global architectures, or ideotypes, that are best suited for resource capture in natural or agricultural environments (14)(15)(16)(17) on the other, but rarely have the two been experimentally connected. Structural-functional models can simulate a range of root architectures based on equations programmed to reproduce local growth and environmental interactions (18)(19)(20), and have been used to predict empirical data (21,22). However realistic parameterization and constraint of these models is thus far piecemeal, lacking adequate empirical data sets that incorporate time dynamics and genetically encoded differences in root development and genetic x environment interactions.…”