Plant Phenomics, From Sensors to Knowledge

Abstract

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

“…The emergence of phenotyping platforms now allows measuring complex traits on thousands of plants, in particular leaf area and biomass (Furbank & Tester, ; Tardieu, Cabrera‐Bosquet, Pridmore, & Bennett, ), light interception (Cabrera‐Bosquet et al, ), plant competitiveness (Chen et al, ), or stomatal conductance (Alvarez Prado et al, ). In particular, it is now possible to dissect biomass accumulation of hundreds of genotypes into components of the Monteith equation, thereby giving the possibility of detection of genomic regions associated with these traits (Chen et al, ).…”

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

“…The first approach is closer to physiological processes and can explicitly take into account the genetic variability of traits as measured in phenotyping platforms, for both main effects and genotype×environment interaction. However, it is often over-parametrized and can lead to inaccurate predictions in some cases because the model follows its own logic even if not applicable to the considered set of conditions (Tardieu et al, 2017). The statistical approach is more conservative and safer, because predicted yields seldom depart from those observed experimentally.…”

“…plant-phenotyping.eu), European Plant Phenotyping Infrastructure (EMPHASIS), and International Plant Phenotyping Network (IPPN; https://www.plant-phenotyping.org/). These infrastructure networks enable access to the necessary tools for phenotyping, in particular robot-assisted image capture (Cooper et al, 2009;Fiorani and Schurr, 2013), statistical designs and models for extracting relevant physiological variables from raw data (Cabrera-Bosquet et al, 2016), and specialized information systems managing large datasets originating from phenotyping experiments (Tardieu et al, 2017). GrowScreen-PaGe is a non-invasive, high-throughput phenotyping system developed at the Institute of Biosciences Accelerating genetic gains in legumes | 3295 (Lee et al, 2015) • 286 (14 wild, 153 landraces and 119 elite; Zhou et al, 2015b) • Illumina 384 SNP VeraCode assays (Lee et al, 2015) • NJAU 355 K SoySNP array • Illumina Infinium SoySNP6K…”

“…Hence, genomewide association studies of traits associated with drought tolerance, based on large-scale germplasm re-sequencing , require a suite of high-throughput and precise phenotyping technologies for capturing, analysing, and interpreting images in order to infer heritable traits that can be analysed genetically (Tardieu et al, 2017).…”

“…It requires (i) capturing information on physiological traits and performance of large numbers of plants, together with their environment, (ii) analysing and organizing the resulting datasets, and (iii) developing models able to disentangle and simulate plant behavior in a range of scenarios (Tardieu et al, 2017). Good and relevant phenotyping starts with a detailed understanding of plant biological processes underlying tolerance/resistance to well-defined constraints.…”