Images Of Root Systems Research Articles

Compound minirhizotron device for root phenotype and water content near root zone

The study of phenotype plays a significant role in plant growth and development. In plant phenotype, the growth state and size distribution of roots are crucial to plant growth and development. Exploring the variation of root development and root zone water content is conducive to improving planting technology with efficiency. Whereas, commercial root phenotype monitoring devices on the market are expensive and sophisticated. This study aims to develop a compound minirhizotron device to acquire images of tomato root system and monitor water content near the root zone. The device mainly included core controller module, transmission module, image acquisition module and moisture monitoring module. The device could realize image acquisition of root system at multiple positions and monitoring of water content near root zone under the condition of less damage to root system. Through image processing, parameters such as root length and root diameter could be calculated. Results indicated that tomato roots at the depth of 6–10 cm from the substrate surface were more densely distributed, and root density decreased with increasing substrate depths. And furthermore, total root length showed a gradual downward trend with the downward movement of the substrate layer. In addition, the closer to the vertical downward straight line from the tomato cultivation site, the greater the root diameter within the monitoring range. In the monitoring results of water content near root zone, the closer to the ground, the higher monitored substrate water content. The overall results showed that the compound minirhizotron device could aquire both root system images and water content near root zone stablely and for a long-term, which could improve the efficiency of planting and irrigation technology.

TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

Background3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture.ResultsWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image.ConclusionsTopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.

Root System Phenotying of Soil-Grown Plants via RGB and Hyperspectral Imaging.

Phenotyping root systems provide essential information for plant breeding, particularly aiming for better abiotic stress resistance. Rhizobox systems provide a field-near growth environment for in situ imaging of root systems in soil. A protocol for RGB and hyperspectral imaging of rhizobox-grown plants is presented that enables gathering of root structural (morphology, architecture) as well as functional (water content, decomposition) information. The protocol exemplifies the setup of a root phenotyping platform combining low-cost RGB with advanced short-wave infrared hyperspectral imaging. For both types of imaging approach, the essential steps of an image analysis pipeline are provided to retrieve biological information on breeding-relevant traits from the imaging datasets.

Erratum to: Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.

GT-RootS: An integrated software for automated root system measurement from high-throughput phenotyping platform images

GT-RootS (Global Traits of Root System) is an automated Java-based open-source solution we are developing for processing root system images provided by the Rhizoscope, a CIRAD phenotyping platform dedicated to dense cereal plants. Two types of use are proposed. The fully-automated mode applies a predefined standard processing pipeline to a preselected set of images while the semi-automated mode allows the user to interactively check and correct intermediate processing results to a specific image. In both cases, GT-RootS combines a local adaptive thresholding algorithm and a similarity indicator to automatically separate the root system from a complex background without user intervention. A covering house-shaped polygon is then defined in the axis system of the root ellipse from vertical weighted density profiles. This canonical shape is composed of both upper trapezoid and lower rectangular compartments from which upper and lower heights, global width and local offset, root system cone angulation and spatial densities can be easily evaluated and displayed. GT-RootS measurements were compared both to expert evaluations and to two other estimation methods on a set of 64 images of a dense Japonica rice root system of 30-days-old plants. We demonstrate also that GT-RootS satisfies the requirements of high-throughput analyses: short processing time (around 30 images per hour on a low-end computer), measurement accuracy and repeatability, and user bias eradication.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines.

Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, three levels of noise were created. This library was used to evaluate the accuracy and usefulness of several image descriptors classically used in root image analysis softwares. Our analysis highlighted that the accuracy of the different traits is strongly dependent on the quality of the images and the type, size, and complexity of the root systems analyzed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic library to improve the estimation of several root system traits. Overall, our analysis is a call to caution when using automatic root image analysis tools. If a thorough calibration is not performed on the dataset of interest, unexpected errors might arise, especially for large and complex root images. To facilitate such calibration, both the image library and the different codes used in the study have been made available to the community.

Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems

Abstract. A better understanding of root–soil interactions and associated processes is essential in achieving progress in crop breeding and management, prompting the need for high-resolution and non-destructive characterization methods. To date, such methods are still lacking or restricted by technical constraints, in particular the charactization and monitoring of root growth and function in the field. A promising technique in this respect is electrical impedance tomography (EIT), which utilizes low-frequency (< 1 kHz)- electrical conduction- and polarization properties in an imaging framework. It is well established that cells and cell clusters exhibit an electrical polarization response in alternating electric-current fields due to electrical double layers which form at cell membranes. This double layer is directly related to the electrical surface properties of the membrane, which in turn are influenced by nutrient dynamics (fluxes and concentrations on both sides of the membranes). Therefore, it can be assumed that the electrical polarization properties of roots are inherently related to ion uptake and translocation processes in the root systems. We hereby propose broadband (mHz to hundreds of Hz) multi-frequency EIT as a non-invasive methodological approach for the monitoring and physiological, i.e., functional, characterization of crop root systems. The approach combines the spatial-resolution capability of an imaging method with the diagnostic potential of electrical-impedance spectroscopy. The capability of multi-frequency EIT to characterize and monitor crop root systems was investigated in a rhizotron laboratory experiment, in which the root system of oilseed plants was monitored in a water–filled rhizotron, that is, in a nutrient-deprived environment. We found a low-frequency polarization response of the root system, which enabled the successful delineation of its spatial extension. The magnitude of the overall polarization response decreased along with the physiological decay of the root system due to the stress situation. Spectral polarization parameters, as derived from a pixel-based Debye decomposition analysis of the multi-frequency imaging results, reveal systematic changes in the spatial and spectral electrical response of the root system. In particular, quantified mean relaxation times (of the order of 10 ms) indicate changes in the length scales on which the polarization processes took place in the root system, as a response to the prolonged induced stress situation. Our results demonstrate that broadband EIT is a capable, non-invasive method to image root system extension as well as to monitor changes associated with the root physiological processes. Given its applicability on both laboratory and field scales, our results suggest an enormous potential of the method for the structural and functional imaging of root systems for various applications. This particularly holds for the field scale, where corresponding methods are highly desired but to date are lacking.

Grasses suppress shoot-borne roots to conserve water during drought

Many important crops are members of the Poaceae family, which develop root systems characterized by a high degree of root initiation from the belowground basal nodes of the shoot, termed the crown. Although this postembryonic shoot-borne root system represents the major conduit for water uptake, little is known about the effect of water availability on its development. Here we demonstrate that in the model C4 grass Setaria viridis, the crown locally senses water availability and suppresses postemergence crown root growth under a water deficit. This response was observed in field and growth room environments and in all grass species tested. Luminescence-based imaging of root systems grown in soil-like media revealed a shift in root growth from crown-derived to primary root-derived branches, suggesting that primary root-dominated architecture can be induced in S. viridis under certain stress conditions. Crown roots of Zea mays and Setaria italica, domesticated relatives of teosinte and S. viridis, respectively, show reduced sensitivity to water deficit, suggesting that this response might have been influenced by human selection. Enhanced water status of maize mutants lacking crown roots suggests that under a water deficit, stronger suppression of crown roots actually may benefit crop productivity.

The significance of image compression in plant phenotyping applications.

We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future.

Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification.

BackgroundRoots are vital to plants for soil exploration and uptake of water and nutrients. Root performance is critical for growth and yield of plants, in particular when resources are limited. Since roots develop in strong interaction with the soil matrix, tools are required that can visualize and quantify root growth in opaque soil at best in 3D. Two modalities that are suited for such investigations are X-ray Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Due to the different physical principles they are based on, these modalities have their specific potentials and challenges for root phenotyping. We compared the two methods by imaging the same root systems grown in 3 different pot sizes with inner diameters of 34 mm, 56 mm or 81 mm.ResultsBoth methods successfully visualized roots of two weeks old bean plants in all three pot sizes. Similar root images and almost the same root length were obtained for roots grown in the small pot, while more root details showed up in the CT images compared to MRI. For the medium sized pot, MRI showed more roots and higher root lengths whereas at some spots thin roots were only found by CT and the high water content apparently affected CT more than MRI. For the large pot, MRI detected much more roots including some laterals than CT.ConclusionsBoth techniques performed equally well for pots with small diameters which are best suited to monitor root development of seedlings. To investigate specific root details or finely graduated root diameters of thin roots, CT was advantageous as it provided the higher spatial resolution. For larger pot diameters, MRI delivered higher fractions of the root systems than CT, most likely because of the strong root-to-soil contrast achievable by MRI. Since complementary information can be gathered with CT and MRI, a combination of the two modalities could open a whole range of additional possibilities like analysis of root system traits in different soil structures or under varying soil moisture.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0060-z) contains supplementary material, which is available to authorized users.

A scanner system for high-resolution quantification of variation in root growth dynamics of Brassica rapa genotypes.

The potential exists to breed for root system architectures that optimize resource acquisition. However, this requires the ability to screen root system development quantitatively, with high resolution, in as natural an environment as possible, with high throughput. This paper describes the construction of a low-cost, high-resolution root phenotyping platform, requiring no sophisticated equipment and adaptable to most laboratory and glasshouse environments, and its application to quantify environmental and temporal variation in root traits between genotypes of Brassica rapa L. Plants were supplied with a complete nutrient solution through the wick of a germination paper. Images of root systems were acquired without manual intervention, over extended periods, using multiple scanners controlled by customized software. Mixed-effects models were used to describe the sources of variation in root traits contributing to root system architecture estimated from digital images. It was calculated that between one and 43 replicates would be required to detect a significant difference (95% CI 50% difference between traits). Broad-sense heritability was highest for shoot biomass traits (>0.60), intermediate (0.25–0.60) for the length and diameter of primary roots and lateral root branching density on the primary root, and lower (<0.25) for other root traits. Models demonstrate that root traits show temporal variations of various types. The phenotyping platform described here can be used to quantify environmental and temporal variation in traits contributing to root system architecture in B. rapa and can be extended to screen the large populations required for breeding for efficient resource acquisition.

Recovering root system traits using image analysis exemplified by two-dimensional neutron radiography images of lupine.

Root system traits are important in view of current challenges such as sustainable crop production with reduced fertilizer input or in resource-limited environments. We present a novel approach for recovering root architectural parameters based on image-analysis techniques. It is based on a graph representation of the segmented and skeletonized image of the root system, where individual roots are tracked in a fully automated way. Using a dynamic root architecture model for deciding whether a specific path in the graph is likely to represent a root helps to distinguish root overlaps from branches and favors the analysis of root development over a sequence of images. After the root tracking step, global traits such as topological characteristics as well as root architectural parameters are computed. Analysis of neutron radiographic root system images of lupine (Lupinus albus) grown in mesocosms filled with sandy soil results in a set of root architectural parameters. They are used to simulate the dynamic development of the root system and to compute the corresponding root length densities in the mesocosm. The graph representation of the root system provides global information about connectivity inside the graph. The underlying root growth model helps to determine which path inside the graph is most likely for a given root. This facilitates the systematic investigation of root architectural traits, in particular with respect to the parameterization of dynamic root architecture models.

Exploring the interacting effect of soil texture and bulk density on root system development in tomato (Solanum lycopersicum L.)

Knowledge of the responses of root systems in horizoned heterogeneous soil is vital to optimise uptake of water and nutrients to maximise crop productivity. We explored the interacting effects of soil bulk density and texture on the development of root systems in tomato.Two main techniques were employed, X-ray micro-Computed Tomography (μCT), to provide non-destructive, three-dimensional (3D) images of root systems in situ and destructive root washing followed by WinRHIZO® scanning. Solanum lycopersicum L. cv. Ailsa Craig plants were grown in soil columns for 10 days to measure the effect of soil compaction on selected root traits. Treatments included bulk density (1.2–1.6Mgm−3), soil texture (loamy sand and clay loam) and the effects of layering.The effect of bulk density on root growth was greatest 3 days after transplanting (DAT) in both soil types. The effect of soil texture was not apparent at this stage, but was significant at 10 DAT for most root and shoot variables. The influence of bulk density differed between soil types as increasing compaction promoted plant growth in clay loam but retarded root growth in loamy sand.We observed that at 3 DAT root growth is primarily influenced by bulk density but by 10 DAT a switch in the processes regulating root growth occurs and the texture of the soil becomes very influential. Future investigations of root growth must consider soil physical properties individually and at specific time points, as their importance changes as the root system becomes established. Here we have demonstrated both positive and negative impacts across a wide range of bulk density treatments in different soil textures on root growth. This illustrates the importance of understanding the complex nature of root–soil interactions, especially for agricultural practices such as seedbed preparation.

GiA Roots: software for the high throughput analysis of plant root system architecture

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

A novel image-analysis toolbox enabling quantitative analysis of root system architecture.

We present in this paper a novel, semiautomated image-analysis software to streamline the quantitative analysis of root growth and architecture of complex root systems. The software combines a vectorial representation of root objects with a powerful tracing algorithm that accommodates a wide range of image sources and quality. The root system is treated as a collection of roots (possibly connected) that are individually represented as parsimonious sets of connected segments. Pixel coordinates and gray level are therefore turned into intuitive biological attributes such as segment diameter and orientation as well as distance to any other segment or topological position. As a consequence, user interaction and data analysis directly operate on biological entities (roots) and are not hampered by the spatially discrete, pixel-based nature of the original image. The software supports a sampling-based analysis of root system images, in which detailed information is collected on a limited number of roots selected by the user according to specific research requirements. The use of the software is illustrated with a time-lapse analysis of cluster root formation in lupin (Lupinus albus) and an architectural analysis of the maize (Zea mays) root system. The software, SmartRoot, is an operating system-independent freeware based on ImageJ and relies on cross-platform standards for communication with data-analysis software.

Soil compaction: a review of past and present techniques for investigating effects on root growth

Soil compaction has been known to affect root growth for millennia. Root growth in natural soils is complex and soil compaction induces several stresses which may interact simultaneously, including increased soil strength, decreased aeration and reduced hydraulic conductivity. Yet, moderate soil compaction offers some benefits to growing roots by increasing root-soil contact so they can extract adequate resources. Until now, improving our understanding of the specific responses of roots to below-ground stimuli has been difficult. However, the advent of new technologies and practices, including X-ray computed tomography, to provide non-destructive, three-dimensional images of root systems throughout the plant's lifecycle now allows the responses of roots encountering changes in their physical, chemical or biotic environment to be established directly and non-invasively. Previous destructive methods, such as root washing, were incapable of identifying and characterising fine root architectural characteristics as these are inextricably linked to the composition of the soil matrix. X-ray computed tomography coupled with genetic approaches will provide a more comprehensive appreciation of the effect of soil compaction on root growth, and the knowledge required to generate improvements in plant breeding programmes and crop husbandry.

Using digital image analysis to quantify the architectural parameters of roots grown in thin rhizotrons

Roots grown in small chambers or rhizotrons combined with image analysis could be effective for screening traits of root morphology and architecture prior to field experimentation. The objective of this study was to evaluate and compare digital images of root systems with conventional root scans (RSCN) for several sorghum varieties grown in rhizotrons. Plants were grown in duplicate in slim (1.5‐cm thick) rhizotrons angled 15° to the vertical, filled with sandy topsoil and subsoil at field capacity (FC) or 50% available water content (AWC) in a split‐plot design in a glasshouse (16/24°C). Root growth visible on the glass surface was recorded weekly, and roots were washed out 42 and 90 days after sowing for digital imaging of the root systems on pin boards (PBI). The roots were then sub‐sampled to quantify the baseline root parameters from digital RSCN using WinRhizo® software with a flat‐bed scanner. Rhizotron images revealed little of the root system (1–2%) but PBI “recovered” up to 70% of the total root length (TRL) measured by RSCN. A high a priori image contrast, increased image resolution (9.1–12 megapixels) and optimized contrast threshold between roots and background were the key parameters in quantifying the roots from digital images. PBI revealed significant differences in rooting patterns, especially distribution of root length density (RLD) in the profile, which is useful for selecting varieties with improved resource capture. However, the root diameters estimated in the PBI were significantly larger than those measured by RSCN due to lack of contrast, root clustering and overlay.