Advanced phenotyping and phenotype data analysis for the study of plant growth and development

Abstract

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

Non-destructive Phenotypic Analysis of Early Stage Tree Seedling Growth Using an Automated Stereovision Imaging Method

“…For the purposes of this paper, we refer to phenotyping as a method to measure plant growth using non-invasive technologies that have become increasingly available in recent years (Fiorani and Schurr, 2013). Unfortunately, measurements of relative growth rate on a mass basis still depend on destructive and time-consuming approaches (Walter et al, 2007; Fiorani and Schurr, 2013; Humplík et al, 2015) with the result of limiting the possibility to examine (1) a large number of samples enabling metadata analysis, and (2) the same sample repeatedly over time (Furbank and Tester, 2011; Busemeyer et al, 2013; Rahaman et al, 2015). To overcome these constraints and to increase the usefulness of phenotype investigation, new approaches based upon the use of technologically advanced equipment that do not affect the samples under examination have been attempted (Tsaftaris and Noutsos, 2009; Walter et al, 2015).…”

Detecting Drought Stress in Soybean Plants Using Hyperspectral Fluorescence Imaging

“…Global food security is of deep concern because of climate change, rapid human population growth, and reduced arable land around the world (Rahaman et al, 2015). In the USA in 2012, 87% of soybean and corn arable lands were affected by drought, causing soybean and corn production to plummet by 25~30%; in turn, this caused an increase in global market prices for the crops.…”

Papaya recombinant inbred lines selection by image-based phenotyping

“…The phenotypic assessment of fruit tree genotypes requires intensive labor and is based mainly on traits such as fruit yield and quality. Conventional phenotyping methodologies have low performance; they are laborious, time-consuming, expensive and, most of the time, destructive (Rahaman et al, 2015). Thus, phenotypic assessment affects the selection strategies and it is necessary to the development of methodologies which will efficiently collect, store and analyze data (Merk et al, 2012).…”