A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field

Wasson, Anton; Bischof, Leanne; Zwart, Alec; Watt, Michelle

doi:10.1093/jxb/erv570

Cited by 66 publications

(61 citation statements)

References 43 publications

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“…Coring [9], and 'shovelomics' [59] are widely used invasive methods (see Figure 2 of [47] for contrast between these methods). Both methods have a throughput comparable with the rhizotron and MRI systems highlighted in Figure 2: per hour, 15 cores can be taken and imaged with three people [60] and eight plots can be shoveled, washed, and imaged per person (L. York, personal communication). Coring, in-field core-break, in-field automated imaging of core faces [60], and Bayesian hierarchical nonlinear mixed modeling, provide root counts in soil over soil depths, which can be treated as a single heritable function [61].…”

Section: Field Phenotypingmentioning

confidence: 99%

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Tracy

Nagel

Postma

et al. 2020

Trends in Plant Science

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Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today's phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.Root system traits (see Glossary) have long been a key target by researchers and breeders for crop improvement [1,2]. Root system architecture supplies water and nutrients for photosynthesis and growth, stabilizes the plant, and prevents soil toxic elements and pathogens from entering leaves and reproductive organs. Roots also host soil microorganisms that can contribute to plant growth and resource efficiency and modulate the supply of resources and signals to shoots, influencing partitioning to organs, including flowers, seed, and fruit. Despite the challenges that plant roots present for measurement, root traits have been incorporated into crops using phenotyping. The observation of roots using phenotyping is central to the discovery of root traits beneficial to crops, their incorporation into new cultivars using prebreeding [3,4], and to their management using precision agriculture. Highlights in Root PhenotypingThe 11 programs highlighted in Figure 1 (Key Figure) exemplify root traits incorporated into new genotypes by phenotyping to increase crop productivity. The examples cover the two main plant types and root system developments: monocotyledons (two major cereal crops, wheat and rice) with seed and stem-borne roots with no cambial thickening, and dicotyledons (two major legume crops, bean

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Section: Field Phenotypingmentioning

confidence: 99%

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Tracy

Nagel

Postma

et al. 2020

Trends in Plant Science

Self Cite

177

128

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“…Each soil core sampled was partitioned in the field into five-centimeter segments from which the number of roots, y, was determined every 10 cm up to 180 cm using a fluorescence imaging system [13]. Each value of y at Depth t(= 1, ..., n D where n D = 18) is the sum of the count imaged from the bottom face of the segment above t and that from the top face of the segment below t. (See Section 2.4 for details of data collection.)…”

Section: Data and Modeling Frameworkmentioning

confidence: 99%

“…Hence, the root counts on the adjoining faces can be regarded as independent values which, when combined to form y, represent the number of roots traversing the break plane at that depth. The fluorescence imaging system generates root counts [13] which necessarily differ from an observers manual counts, although both are subject to measurement error. The raw imaging data were processed (available from Supplementary Material) and visualizations produced with the statistical programming language R [21] using the packages 'dplyr [22] and 'ggplot2 [23].…”

Section: Data Collectionmentioning

confidence: 99%

“…The counts correlate with the root length in the corresponding volume of soil. This technique has been used to phenotype root count distributions in 43 genotypes [12] and efforts have been made to automate the root count process [13] to reduce the labor requirements. However, root counts from the core-break method are subject to a high degree of variation between samples [11], which makes it challenging to identify genotype-specific traits from root counts or to associate genotypes with discernible properties of root count profiles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rooting density differentiates wheat genotypes through Bayesian modeling

et al. 2016

Preprint

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Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a Bayesian hierarchical nonlinear modeling approach that utilizes the complete field data for wheat genotypes to fit an idealized relative intensity function for the root distribution over depth. Our approach was used to determine heritability: how much of the variation between field samples was purely random versus being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our Bayesian analysis led to denoised profiles which exhibited rigorously discernible phenotypic traits. The profile-specific traits could be representative of a genotype and thus can be used as a quantitative tool to associate phenotypic traits with specific genotypes.

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“…The approaches include: (1) destructive methods involving the separation of entire root systems from soil, either by in situ excavation (Mackie‐Dawson & Atkinson, ) or by ex situ root washing (Bucksch et al ., ); (2) destructive methods (e.g. coring) which sample a small fraction of the roots (Wasson et al ., ); (3) use of a soil substitute for growth, typically transparent, that allows for in situ root observation – examples include transparent soil (Downie et al ., ) and agar/agar‐like systems (Clark et al ., ); (4) noninvasive imaging of root systems through a transparent surface using a standard camera (Belter & Cahill, ), a minirhizotron (McNickle & Cahill, ; Karst et al ., ) or a scanner (Adu et al ., ); and (5) noninvasive imaging capable of soil surface penetration, such as magnetic resonance imaging (MRI) (Metzner et al ., ) and computed tomography (CT) scanning (Lontoc‐Roy et al ., ; Flavel et al ., ). 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 ., ; Hatzig et al ., ; Kalogiros et al ., ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Rapidly determining root growth patterns is biologically important and technically challenging. Current methods focus on direct observation of roots and require destructive excavations or time-consuming root tracing. We developed a novel methodology based on analyzing soil particle displacement, rather than direct observation of roots. This inferred root growth method uses digital image correlation (DIC) analysis, an established and high-throughput method used in many engineering and science disciplines. By applying DIC analyses to repeated images of plants grown in clear window boxes, we produced visually intuitive and quantifiable strain maps, indicating the magnitude and direction of soil movement. From this, we could infer root growth and rapidly quantify root system metrics. Strain measures were closely associated with the spatial distribution of roots and correlated with root length measured using conventional approaches. The method also allowed for the detection of root proliferation in nutrient-enriched soil patches, indicating its suitability for quantifying biological patterns. This novel application of DIC in root biology is effective, scalable, low cost, flexible and complementary to existing technologies. This method offers a new tool for answering questions in plant biology and will be particularly useful in studies involving temporal dynamics of root processes.

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A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field

Abstract: HighlightFluorescence imaging was built into a portable box called BlueBox, and roots in soil cores were directly and accurately quantified by automated image analysis, allowing root phenotyping in the field for pre-breeding.

Cited by 66 publications

References 43 publications

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Rooting density differentiates wheat genotypes through Bayesian modeling

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

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