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Abstract Detail



Bioinformatic and Biometric Methods in Plant Morphology

Das, Abhiram [1], Weitz, Joshua S. [2], Lynch, Jonathan [3], Bucksch, Alexander [4].

Digging into Root Traits (DIRT) – An Online Phenotyping Platform for Analysis of Root Images in the Field.

Plant root systems are key drivers of plant function and yield [1]. They are also under-explored targets to meet global food and energy demands [2]. New technologies have been developed to characterize crop root system architecture (CRSA). These technologies potentially accelerate the progress in understanding the genetic control and environmental response of CRSA. To realize this potential, requires new methods for analysis of image data of crop root systems [3, 4]. Prior approaches have focused on the estimation of root traits from images, yet no integrated platform exists that allows easy access to trait analysis methods combined with storage solutions linked to meta data. In this talk, we describe DIRT, a platform designed to support root trait estimation from images taken under field conditions. DIRT enables biologists to store, process and share their root images through a web interface. In DIRT a user can organize images into collections per experiment, execute image processing algorithms, and view processed images and their estimated trait values. The user also has the option to download processed results for further analysis. For each image we store the settings and corresponding results each time the image was chosen for processing. As an illustration of DIRT, we provide a case study of its deployment in the analysis of crop root images. Phenotyping of mature root systems in situ involves tradeoffs between invasive and noninvasive methodologies for large scale root phenotyping. Hence, we pair DIRT with algorithms that adapt “Shovelomics” [5], which quantifies CRSA manually with a standardized scoring scheme. Typically an individual can phenotype up to 100 samples per day. Here, we show that DIRT implements novel algorithms to extract CRSA traits from images of mature root systems, accelerating both the speed of trait estimation and broadening the range of traits we are able to estimate. [1] Lynch J (1995) Root architecture and plant productivity. Plant Physiology 109(1):7-13. [2] Furbank RT & Tester M (2011) Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16(12):635-644. [3] Clark RT, et al. (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiology 156(2):455-465. [4] Galkovskyi T, et al. (2012) GiA Roots: Software for the high throughput analysis of plant root system architecture. BMC Plant Biology 12(116). [5] Trachsel S, Kaeppler SM, Brown KM, & Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant and Soil 341(1-2):75-87.

Broader Impacts:


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Related Links:
Homepage of the Weitz Lab
Homepage of Alexander Bucksch
DIRT prototype


1 - Georgia Institute of Technology, School of Biology, 310 Ferst Drive, Atlanta, GA, 30332, USA
2 - Georgia Institute of Technology, School of Biology and School of Physics, 310 Ferst Drive, Atlanta, GA, 30332, USA
3 - Pennsylvania State University, DEPT OF HORTICULTURE, 102 Tyson Building, UNIVERSITY PARK, PA, 16802-4201, USA
4 - Georgia Institute of Technology, School of Biology and School of Interactive Computing, 310 Ferst Drive, Cherry Emerson 237, Atlanta, GA, 30332, USA

Keywords:
Root Phenotyping
image analysis
web application.

Presentation Type: Symposium or Colloquium Presentation
Session: C2
Location: Prince of Wales/Riverside Hilton
Date: Monday, July 29th, 2013
Time: 2:15 PM
Number: C2004
Abstract ID:688
Candidate for Awards:None


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