Accelerating root system phenotyping of seedlings through a computer‑assisted processing pipeline

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Dupoy, Lionel X., Wright, Gladys, Thompson, Jacqueline A., Taylor, Anna, Dekeyser, Sebastien, White, Christopher P., Thomas, Wiliam T.B., Nightingale, Mark, Hammond, John P., Graham, Neil S., Thomas, Catherine L., Broadley, Martin R. and White, Philip J. (2017) Accelerating root system phenotyping of seedlings through a computer‑assisted processing pipeline. Plant Methods, 13 (57). pp. 1-14. ISSN 1746-4811

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Official URL: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-017-0207-1

Abstract

Background: There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.

Results: Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database.

Conclusions: Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

Item Type:

Article

RIS ID:

https://nottingham-repository.worktribe.com/output/872322

Keywords:

Root, Phenotyping, Error, Pipeline, Barley, Brassica

Schools/Departments:

University of Nottingham, UK > Faculty of Science > School of Biosciences

Identification Number:

https://doi.org/10.1186/s13007-017-0207-1

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