Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time.

E H Neilson,B Lindberg Møller,B Berger,B Berger,C K Blomstedt,R M Gleadow,A M Edwards

doi:10.1093/jxb/eru526

Abstract

The use of high-throughput phenotyping systems and non-destructive imaging is widely regarded as a key technology allowing scientists and breeders to develop crops with the ability to perform well under diverse environmental conditions. However, many of these phenotyping studies have been optimized using the model plant Arabidopsis thaliana. In this study, The Plant Accelerator(®) at The University of Adelaide, Australia, was used to investigate the growth and phenotypic response of the important cereal crop, Sorghum bicolor L. Moench and related hybrids to water-limited conditions and different levels of fertilizer. Imaging in different spectral ranges was used to monitor plant composition, chlorophyll, and moisture content. Phenotypic image analysis accurately measured plant biomass. The data set obtained enabled the responses of the different sorghum varieties to the experimental treatments to be differentiated and modelled. Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index. Analysis of colour images revealed that leaf 'greenness' correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content. It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits. R scripts for robust, parsimonious models are provided to allow other users of phenomic imaging systems to extract useful data readily, and thus relieve a bottleneck in phenotypic screening of multiple genotypes of key crop plants.

Highlights

In order to feed an increasing global population, crops must be bred that are able to withstand and perform well under adverse environmental conditions, are resistant to herbivores and pathogens, and have increased nutritional value, and a reduced demand for agricultural inputs such as a fertilizer (Tester and Langridge, 2010; Gregory and George, 2011)
Analysis of colour images revealed that leaf ‘greenness’ correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content
Phenotypic databases are rapidly growing for the genetic model plant Arabidopsis (Arvidsson et al, 2011; Pieruschka and Poorter, 2012), but there is an unmet need for similar databases to be developed for major grain crops, such as wheat, barley, and sorghum

Summary

Introduction

In order to feed an increasing global population, crops must be bred that are able to withstand and perform well under adverse environmental conditions, are resistant to herbivores and pathogens, and have increased nutritional value, and a reduced demand for agricultural inputs such as a fertilizer (Tester and Langridge, 2010; Gregory and George, 2011). Sophisticated imaging technologies and platforms that measure plant growth and performance in an automated, high-throughput, non-destructive manner use cameras operating in the visible light range that generate two-dimensional images to track plant growth and health (Rajendran et al, 2009; Arvidsson et al, 2011; Golzarian et al, 2011). It is important that the outputs from these imaging platforms are bench-marked against traditional measures, so that they can be integrated with the wealth of phenotypic data already accumulated over many years of crop breeding. This requires that the models be experimentally verified for particular crops and a range of environmental conditions (Furbank and Tester, 2011; Fiorani et al, 2012)

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Conclusion