Focus Issue on Roots: Image-Based High-Throughput Field Phenotyping of Crop Roots

Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.Crop root systems represent an underexplored target for improvements as part of community efforts to ensure that global crop yields and productivity keep pace with population growth (Godfray et al., 2010; Gregory and George, 2011; Nelson et al., 2012). The challenge in improving crop root systems is that yield and productivity also depend on soil fertility, which is also a major constraint to global food production (Lynch, 2007). Hence, desired improvements to crop root systems include enhanced water use efficiency and water acquisition given the increased likelihood of drought in future climates (Intergovernmental Panel on Climate Change, 2014). Over the long term, the development of crop genotypes with improved root phenotypes requires advances in the characterization of root system architecture (RSA) and in the relationship between RSA and function.The emerging discipline of plant phenomics aims to expand the scope, throughput, and accuracy of plant trait estimates (Furbank, 2009). In the case of plant roots, structural traits may describe RSA as geometric or topological measures of the root shape at various scales (e.g. diameters and width of the whole root system or a single branch; Lynch, 1995; Den Herder et al., 2010). These traits can be used to predict yield under specific conditions such as drought or low fertility. Understanding the diversity and development of root architectural traits is crucial, because spatial and temporal root deployment affects plant fitness, especially water and nutrient acquisition (Rich and Watt, 2013). Thus, improving plant performance may benefit from improvements in the characterization of root architecture, including understanding how trait variation arises as a function of genotype and environmental conditions (Band et al., 2012; Shi et al., 2013).Current efforts to understand the structure of crop root systems have already led to a number of imaging solutions (Lobet et al., 2013) that are able to extract root architecture traits under various conditions (Fiorani et al., 2012), including laboratory conditions (de Dorlodot et al., 2007) in which plants are often grown in pots or glass containers (Zeng et al., 2008; Armengaud et al., 2009; Le Bot et al., 2010; Clark et al., 2011; Lobet et al., 2011; Naeem et al., 2011; Galkovskyi et al., 2012). In the case of pots, expensive magnetic resonance imaging technologies represent one noninvasive approach to capture high-resolution details of root architecture (Schulz et al., 2013), similar to the capabilities of x-ray microcomputed tomography (μCT) systems. X-ray systems allow capturing of the root architecture at a fine scale in containers with a wide variety of soil types (Mairhofer et al., 2012; Mooney et al., 2012). It has been shown that x-ray μCT paired with specifically designed algorithms has sufficient resolution to recover the root structure in many cases (Mairhofer et al., 2013). Nevertheless, x-ray μCT systems are currently unable to image mature root systems because of technical restrictions in container size.As an alternative, root systems can be imaged directly with a digital camera when grown in glass containers with transparent media such as gellan gum or transparent soil replacements (Downie et al., 2012). Such in situ imaging benefits from controlled lightning conditions during image acquisition, even more so when focusing on less complex root structures of younger plants that allow three-dimensional reconstruction (Clark et al., 2011; Topp et al., 2013). Under such controlled conditions, it is expected that imaging would enable the study of growth of roots over time (Spalding and Miller, 2013; Sozzani et al., 2014).However, all of the above-listed solutions have been used primarily to assess root structures in the early seedling stage (French et al., 2009; Brooks et al., 2010; Sozzani et al., 2014) to approximately 10 d after germination (Clark et al., 2011), which makes it all but impossible to directly observe mature root systems. For example, primary and seminal roots make up the major portion of the seedling root system in maize (Zea mays) during the first weeks after germination. Later in development, postembryonic shoot-borne roots become the major component of the maize root system (Hochholdinger, 2009), not yet accessible to laboratory phenotyping platforms. In addition to phenological limitations, current phenotyping approaches for root architecture require specialized growth conditions with aerial and soil environments that differ from field conditions; the effects of such differences on RSA are only sparsely reported in literature (Hargreaves et al., 2009; Wojciechowski et al., 2009).Indeed, high-throughput field phenotyping can be seen as a new frontier for crop improvement (Araus and Cairns, 2014) because imaging a mature root system under realistic field conditions poses unique challenges and opportunities (Gregory et al., 2009; Zhu et al., 2011; Pieruschka and Poorter, 2012). Challenges are intrinsic to roots grown in the field because the in situ belowground imaging systems to date are unable to capture fine root systems. As a consequence, initial attempts to characterize root systems in the field focused on the manual extraction of structural properties. Manual approaches analyzed the root system’s branching hierarchy in relation to root length and rooting depth (Fitter, 1991). In the late 1980s, imaging techniques were first used (Tatsumi et al., 1989) to estimate the space-filling behavior of roots, an estimation process that was recently automated (Zhong et al., 2009). A weakness of such approaches is that exact space-filling properties, such as the fractal dimension, are sensitive to the incompleteness of the excavated root network (Nielsen et al., 1997, 1999). In particular, the box counting method was criticized for above-ground branching networks of tree crowns (Da Silva et al., 2006). The same critiques apply to root systems, because fine secondary or tertiary roots can be lost or cutoff or can adhere to each other during the cleaning process, making it impossible to analyze the entire network.As an alternative, the shovelomics field protocol has been proposed to characterize the root architecture of maize under field conditions (Trachsel et al., 2011). In shovelomics, the researcher excavates the root at a radius of 20 cm around the hypocotyl and 20 cm below the soil surface. This standardized process captures the majority of the root system biomass within the excavation area. After excavation, the shoot is separated from the root 20 cm above the soil level and washed in water containing mild detergent to remove soil. The current procedure places the washed root on a phenotyping board consisting of a large protractor to measure dominant root angles with the soil level at depth intervals and marks to score length and density classes of lateral roots. A digital caliper is used to measure root stem diameters (Fig. 1). Observed traits vary slightly from crop to crop but generally fit into the following categories by depth or root class: angle, number, density, and diameter. In this way, field-based shovelomics allows the researcher to visually quantify the excavated structure of the root crown and compare genotypes via a common set of traits that do not depend on knowledge or observation of the entire root system network. Of note, shovelomics is of particular use in developing countries, which have limited access to molecular breeding platforms (Delannay et al., 2012), and for which direct phenotypic selection is an attractive option.Open in a separate windowFigure 1.A, Classic shovelomics scoring board to score the angle of maize roots with the soil tissue. B, An example to score rooting depth and angle in common bean.Nonetheless, direct phenotypic selection for root traits of field-grown root systems comes with a number of caveats and drawbacks. To date, the quantification of mature root systems is highly dependent on the researcher, reducing the repeatability of measured quantities (Gil et al., 2007). In addition, manual approaches impose limitations on both the number of accessible traits and the number of samples collected. For example, a typical shovelomics phenotyper can gather 10 to 12 traits from a sample in 2 min or approximately 200 samples in a typical workday (Trachsel et al., 2011). This means that evaluating a statistically more significant trial of 1,000 samples may take 5 d or more. Such a time span introduces variation owing to plant growth and phenology. On the other hand, shovelomics is adaptable to both monocot and dicot roots; therefore, its procedure can be scaled over the huge variety of root morphologies in dicots and monocots. For these reasons, we contend that shovelomics represents a valued target for an automated high-throughput phenotyping approach in the field.We introduce an imaging approach for high-throughput phenotyping of mature root systems under realistic soil conditions to address the limitations of manual data collection (Fig. 2, I and II). Our new algorithms directly extract root architecture traits from excavated root images (Fig. 2, III and IV). Rapid imaging of the roots and decoupled analysis of the collected data at a later time improve both the throughput and synchronization of experiments. In addition to traits that are developed from shovelomics, our algorithms extract previously inaccessible traits such as root tip diameter, spatial distribution, and root tissue angle (RTA). Overall, our approach allows imaging directly at field sites with an easily reproducible imaging setup and involves no transportation cost or expensive hardware. We demonstrate the utility of our approach in the field-based analysis of maize and cowpea (Vigna unguiculata) architecture.Open in a separate windowFigure 2.I, Imaging board on the example of a maize root. The experiment tag is used to capture an experiment number, and the scale marker allows the correction of camera tilting and transforming image coordinates into metric units. II, Camera mounted on a tripod placed on top of the imaging board coated with blackboard paint. Note that images were taken with protection against direct sunlight not shown in the image. III, Example of the segmentation of the original image into a binary image and then into a series of image masks that serve as input to estimate traits for monocot and dicot roots. The sample is that of a maize root, 40 d after planting at the URBC. IV, The imaging pipeline for dicot roots and sparse monocot roots: Original image on the imaging board (a), derived distance map where the lighter gray level represents a larger diameter of the imaged object (b), medial axis includes loops (c), and loop RTP with a sample of the root branching structure (d). Colors are randomly assigned to each path. The sample is that of a cowpea root, approximately 30 d after planting at the URBC.