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Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high throughput techniques based on machine vision, robotics and computing (Plant Phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focusses on how field based plant phenomics can enable next generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from “Green Revolution” traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, chlorophyll fluorescence and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented. This article is protected by copyright. All rights reserved.

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR ( of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association ( = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass ( = 0.93 and = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.

Improved genotypic performance in water-limited environments relies on traits, like ‘stay-green’, that are robust and repeatable, correlate well across a broader range of target environments and are genetically more tractable than assessment of yield per se. Christopher et al. (see pages 5159–5172) used multi-temporal, Normalised Difference Vegetative Index (NDVI) measurements with crop simulation modelling to demonstrate the value of various stay-green phenotype parameters for improving grain yield across different environment types.

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

Infrared canopy temperature (CT) is a well-established surrogate measure of stomatal conductance. There is ample evidence showing that genotypic variation in stomatal conductance is associated with grain yield in wheat. Our goal was to determine when CT repeatability is greatest (throughout the season and within the day) to guide CT deployment for research and wheat breeding. CT was measured continuously with ArduCrop wireless infrared thermometers from post-tillering to physiological maturity, and with airborne thermography on cloudless days from manned helicopter at multiple times before and after flowering. Our experiments in wheat, across two years contrasting for water availability, showed that repeatability for CT was greatest later in the season, during grain-filling, and usually in the afternoon. This was supported by the observation that repeatability for ArduCrop, and more so for airborne CT, was significantly associated ( < 0.0001) with calculated clear-sky solar radiation and to a lesser degree, vapor pressure deficit. Adding vapor pressure deficit to a model comprising either clear-sky solar radiation or its determinants, day-of-year and hour-of-day, made little to no improvement to the coefficient of determination. Phenotypic correlations for airborne CT afternoon sampling events were consistently high between events in the same year, more so for the year when soil water was plentiful ( = 0.7 to 0.9) than the year where soil water was limiting ( = 0.4 to 0.9). Phenotypic correlations for afternoon airborne CT were moderate between years contrasting in soil water availability ( = 0.1 to 0.5) and notably greater on two separate days following irrigation or rain in the drier year, ranging from = 0.39 to 0.53 ( < 0.0001) for the midday events. For ArduCrop CT the pattern of phenotypic correlations, within a given year, was similar for both years: phenotypic correlations were higher during the grain-filling months of October and November and for hours-of-day from 11 onwards. The lowest correlations comprised events from hours-of-day 8 and 9 across all months. The capacity for the airborne method to instantaneously sample CT on hundreds of plots is more suited to large field experiments than the static ArduCrop sensors which measure CT continuously on a single experimental plot at any given time. Our findings provide promising support for the reliable deployment of CT phenotyping for research and wheat breeding, whereby the high repeatability and high phenotypic correlations between afternoon sampling events during grain-filling could enable reliable screening of germplasm from only one or two sampling events.

One way to increase yield potential in wheat is screening for natural variation in photosynthesis. This study uses measured and modelled physiological parameters to explore genotypic diversity in photosynthetic capacity (P c, Rubisco carboxylation capacity per unit leaf area at 25 °C) and efficiency (P eff, P c per unit of leaf nitrogen) in wheat in relation to fertilizer, plant stage, and environment. Four experiments (Aus1, Aus2, Aus3, and Mex1) were carried out with diverse wheat collections to investigate genetic variation for Rubisco capacity (V cmax25), electron transport rate (J), CO₂ assimilation rate, stomatal conductance, and complementary plant functional traits: leaf nitrogen, leaf dry mass per unit area, and SPAD. Genotypes for Aus1 and Aus2 were grown in the glasshouse with two fertilizer levels. Genotypes for Aus3 and Mex1 experiments were grown in the field in Australia and Mexico, respectively. Results showed that V cmax25 derived from gas exchange measurements is a robust parameter that does not depend on stomatal conductance and was positively correlated with Rubisco content measured in vitro. There was significant genotypic variation in most of the experiments for P c and P eff. Heritability of P c reached 0.7 and 0.9 for SPAD. Genotypic variation and heritability of traits show that there is scope for these traits to be used in pre-breeding programmes to improve photosynthesis with the ultimate objective of raising yield potential.

Background To our knowledge, there is no software or database solution that supports large volumes of biological time series sensor data efficiently and enables data visualization and analysis in real time. Existing solutions for managing data typically use unstructured file systems or relational databases. These systems are not designed to provide instantaneous response to user queries. Furthermore, they do not support rapid data analysis and visualization to enable interactive experiments. In large scale experiments, this behaviour slows research discovery, discourages the widespread sharing and reuse of data that could otherwise inform critical decisions in a timely manner and encourage effective collaboration between groups. Results In this paper we present SensorDB, a web based virtual laboratory that can manage large volumes of biological time series sensor data while supporting rapid data queries and real-time user interaction. SensorDB is sensor agnostic and uses web-based, state-of-the-art cloud and storage technologies to efficiently gather, analyse and visualize data. Conclusions Collaboration and data sharing between different agencies and groups is thereby facilitated. SensorDB is available online at http://sensordb.csiro.au .

Aim To test for the presence of an impediment to water flow at the soil-root interface. Methods Wheat plants were grown in repacked and undisturbed field soil. Their transpiration rate, E, was varied in several steps from low to high and then back to low again, while the hydrostatic pressure in the leaf xylem, Ψ xylem , was measured non-destructively and continuously. These measurements were compared to a mathematical model that calculated Ψ xylem by assuming that the hydraulic resistance across the plant was constant and that the radial flow of water to unit length of a typical plant root generated gradients in pressure in the soil water. Results For the repacked soil, the radial flow model could not match the experiment during the falling phase of E, unless it was assumed that either an additional, constant, interfacial resistance between the soil and the roots had developed when E was large and Ψ xylem was rapidly falling, or that the resistance within the plant had changed. For the undisturbed field soil, the radial flow model did not agree with the experiment. Plausible agreement was achieved when plant water uptake was accounted for using a distributed sink model in HYDRUS-1D, with E integrated across the rootzone. This approach was based on the measured large variation in the vertical distribution of roots. Conclusions There was no strong evidence of large drawdowns of soil water in the rhizosphere, even when Ψ xylem was falling rapidly when E was large and the soil was moderately dry. Thus, there seems to have been an additional impediment to water flow from soil to plant, either within the plant, or at the interface between the two.

Aims To determine soil water difrusivity, D(θ), on undisturbed field soil at medium to low water content (suction range from 10 to 150 m of water), for the purpose of modeling the uptake of water by plant roots. Methods The method is based on the analysis of onestep outflow induced by a turbulent stream of dry air over the exposed end of a soil core, with the other end of the core enclosed. The outflow is measured through time as the change in the weight of the core as it sits on a recording balance. D(θ)is calculated by deconvoluting the measured outflow function. Results Over the suction range of 10 to 150 m of water, D(θ) calculated on the undisturbed soil ranged from 20×10⁻⁹ to 10×10⁻⁹ [m² S⁻¹], substantially higher than other published estimates over this range in suction. Conclusions These unusually large values cast doubt on the view that flow of water to roots limits uptake of water from the targeted subsoil.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community. “Protein relocalisation plays a major role in the innate immune response but remains incompletely characterised. Here, the authors combine temporal proteomics with LOPIT, a spatial proteomic workflow, in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during the LPS-induced immune response in THP-1 cells.