Similarly, despite some successes, there are relatively few publicly available root phenotyping datasets. In response, a number of national and international efforts, including the International Plant Phenotyping Network, have established “plant phenomics” centers to quantify plant phenotypes and their genetic origin. Indeed, phenotyping rather than genotyping is recognized as the bottleneck limiting advances, given inexpensive next-generation sequencing technologies that have paved the way for characterizing the genotypes of diversity panels of thousands of recombinant inbred lines. Extending field-based studies and sample sizes is a widely shared goal for future phenotyping scenarios. Especially field studies to characterize RSA of mature field-grown crops involve laborious manual tasks that limit the achievable sample size. ĭeveloping new crop varieties includes both laboratory- and field-based studies. Yet, little is known regarding the relationship between root system architecture (RSA) and crop function with few examples linking root phenotype with genotype and phenotypic advantages under given field conditions. Breeding more efficient roots is increasingly recognized as a high-priority target to achieve yield improvements because roots are essential for nutrient and water uptake. Meeting this increased demand requires significant improvements in crop yield and the development of crop plants adapted to water-stress and low fertility soils. Global food demand is projected to double by the year 2050. 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. All stored and computed data is easily accessible to the public and broader scientific community. As such it enables plant scientists to spend more time on science rather than on technology. It makes high-throughput RSA trait computation available to the community with just a few button clicks. ![]() It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. The platform is accessible at and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). ConclusionĭIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. 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. 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. Here, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. 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. 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. ![]() Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. ![]() These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Many new technologies have been developed to characterize crop root system architecture (CRSA). They are also under-explored targets to meet global food and energy demands. Plant root systems are key drivers of plant function and yield.
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