ArticlePlant methods2015
Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics.
Article in Plant methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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Who cites it
78 citing papers in PubMed, 1 synthesis or guideline pooled it, 206 citations in OpenAlex.
- Pearl millet response to drought: A review.Frontiers in plant science · 2023Pooled it
- Genetic architecture and functional consequences of lateral root length in maize.Journal of experimental botany · 2026Article
- Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters.Plant methods · 2026Article
- Phenome-to-genome insights for evaluating root system architecture in field studies of maize.The plant genome · 2025Article
- Seeing the unseen: A novel approach to extract latent plant root traits from digital images.Plant phenomics (Washington, D.C.) · 2025Article
- Identification of phenotypic and transcriptomic signatures underpinning maize crown root systems.Plant phenomics (Washington, D.C.) · 2025Article
- Modulation of polar auxin transport identifies the molecular determinants of source-sink carbon relationships and sink strength in poplar.Tree physiology · 2024Article
- Automated seminal root angle measurement with corrective annotation.AoB PLANTS · 2024Article
- From phenotyping to genetic mapping: identifying water-stress adaptations in legume root traits.BMC plant biology · 2024Review
- Cassava Breeding and Cultivation Challenges in Thailand: Past, Present, and Future Perspectives.Plants (Basel, Switzerland) · 2024Review
- Multi-trait association mapping for phosphorous efficiency reveals flexible root architectures in sorghum.BMC plant biology · 2024Article
- Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.Scientific reports · 2024Article
- The soil microbiome modulates the sorghum root metabolome and cellular traits with a concomitant reduction of Striga infection.Cell reports · 2024Article
- GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.G3 (Bethesda, Md.) · 2024Article
- Hybrid performance evaluation and genome-wide association analysis of root system architecture in a maize association population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A dolabralexin-deficient mutant provides insight into specialized diterpenoid metabolism in maize.Plant physiology · 2023Article
- A method of cotton root segmentation based on edge devices.Frontiers in plant science · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Genetic resources and breeding of maize forFrontiers in plant science · 2023Review
- Iterative image segmentation of plant roots for high-throughput phenotyping.Scientific reports · 2022Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. DESCRIPTION: Here, 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://www.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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.