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.

Root architecture was determined together with shoot parameters under well watered and drought conditions in the field in three soybean cultivars (A5409RG, Jackson and Prima 2000). Morphology parameters were used to classify the cultivars into different root phenotypes that could be important in conferring drought tolerance traits. A5409RG is a drought-sensitive cultivar with a shallow root phenotype and a root angle of <40°. In contrast, Jackson is a drought-escaping cultivar. It has a deep rooting phenotype with a root angle of >60°. Prima 2000 is an intermediate drought-tolerant cultivar with a root angle of 40°-60°. It has an intermediate root phenotype. Prima 2000 was the best performing cultivar under drought stress, having the greatest shoot biomass and grain yield under limited water availability. It had abundant root nodules even under drought conditions. A positive correlation was observed between nodule size, above-ground biomass and seed yield under well-watered and drought conditions. These findings demonstrate that root OPEN ACCESSAgronomy 2014, 4 419 system phenotyping using markers that are easy-to-apply under field conditions can be used to determine genotypic differences in drought tolerance in soybean. The strong association between root and nodule parameters and whole plant productivity demonstrates the potential application of simple root phenotypic markers in screening for drought tolerance in soybean.

BackgroundPlant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0093-3) contains supplementary material, which is available to authorized users.

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Genetic analysis of data produced by novel root phenotyping tools was used to establish relationships between cowpea root traits and performance indicators as well between root traits and Striga tolerance. Selection and breeding for better root phenotypes can improve acquisition of soil resources and hence crop production in marginal environments. We hypothesized that biologically relevant variation is measurable in cowpea root architecture. This study implemented manual phenotyping (shovelomics) and automated image phenotyping (DIRT) on a 189-entry diversity panel of cowpea to reveal biologically important variation and genome regions affecting root architecture phenes. Significant variation in root phenes was found and relatively high heritabilities were detected for root traits assessed manually (0.4 for nodulation and 0.8 for number of larger laterals) as well as repeatability traits phenotyped via DIRT (0.5 for a measure of root width and 0.3 for a measure of root tips). Genome-wide association study identified 11 significant quantitative trait loci (QTL) from manually scored root architecture traits and 21 QTL from root architecture traits phenotyped by DIRT image analysis. Subsequent comparisons of results from this root study with other field studies revealed QTL co-localizations between root traits and performance indicators including seed weight per plant, pod number, and Striga (Striga gesnerioides) tolerance. The data suggest selection for root phenotypes could be employed by breeding programs to improve production in multiple constraint environments.

At the genus and species level, variation in root anatomy and architecture may interact to affect strategies of drought avoidance. To investigate this idea, root anatomy and architecture of the drought-sensitive common bean (Phaseolus vulgaris) and drought-adapted tepary bean (Phaseolus acutifolius) were analyzed in relation to water use under terminal drought. Intraspecific variation for metaxylem anatomy and axial conductance was found in the roots of both species. Genotypes with highconductance root metaxylem phenotypes acquired and transpired more water per unit leaf area, shoot mass, and root mass than genotypes with low-conductance metaxylem phenotypes. Interspecific variation in root architecture and root depth was observed where P. acutifolius has a deeper distribution of root length than P. vulgaris. In the deeper-rooted P. acutifolius, genotypes with high root conductance were better able to exploit deep soil water than genotypes with low root axial conductance. Contrastingly, in the shallower-rooted P. vulgaris, genotypes with low root axial conductance had improved water status through conservation of soil moisture for sustained water capture later in the season. These results indicate that metaxylem morphology interacts with root system depth to determine a strategy of drought avoidance and illustrate synergism among architectural and anatomical phenotypes for root function.

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