Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis  

Abstract

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for highthroughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

Papaya recombinant inbred lines selection by image-based phenotyping

“…The CVe values estimated for PH and SD were low indicating a high degree of experimental precision. Precision in the phenotypic assessment of the morphological traits of plants using methodologies based on digital images has been reported in several economically important crops such as barley (Chen et al, 2014), Australian cedar (Shimizu et al, 2014) and rice (Sritarapipat et al, 2014). However, the CVe values in the remaining traits were moderate, and the highest value was found for the NDF trait (25 and 28 % estimated by MP and IBP, respectively).…”

The quest for understanding phenotypic variation via integrated approaches in the field environment

“…This is largely because imaging and sensor data allow the capture of multivariate information that may not be perceived by humans but is nonetheless essential for understanding plant biology. This can be exemplified by Chen et al (2014), who used high-throughput image analysis to gain insight into drought stress physiology. Using an open-source image-analysis pipeline, the authors were able to extract nearly 400 phenotypic traits from images, but more importantly, they were able to derive new traits of physiological importance that captured growth dynamics as well as stress-responsive traits, something not possible with low-throughput phenotyping.…”

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…• Versatile phenotyping system and analytics platform for diverse temporal responses to water availability Chen et al (2014), Neumann et al (2015) GlyPh (self-construction)…”