The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.

This review considers stomatal conductance as an indicator of genotypic differences in the growth response to water stress. The benefits of using stomatal conductance are compared with photosynthetic rate and other indicators of genetic variation in water stress tolerance, along with the use of modern phenomics technologies. Various treatments for screening for genetic diversity in response to water deficit in controlled environments are considered. There is no perfect medium: there are pitfalls in using soil in pots, and in using hydroponics with ionic and non-ionic osmotica. Use of mixed salts or NaCl is recommended over non-ionic osmotica. Developments in infrared thermography provide new and feasible screening methods for detecting genetic variation in the stomatal response to water deficit in controlled environments and in the field.

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 (r² 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 (r² = 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 (r² = 0.93 and r² = 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.

In recent years, imaging based, automated, non-invasive, and non-destructive high-throughput plant phenotyping platforms have become popular tools for plant biology, underpinning the field of plant phenomics. Such platforms acquire and record large amounts of raw data that must be accurately and robustly calibrated, reconstructed, and analysed, requiring the development of sophisticated image understanding and quantification algorithms. The raw data can be processed in different ways, and the past few years have seen the emergence of two main approaches: 2D image processing and 3D mesh processing algorithms. Direct image quantification methods (usually 2D) dominate the current literature due to comparative simplicity. However, 3D mesh analysis provides the tremendous potential to accurately estimate specific morphological features cross-sectionally and monitor them over-time.

A high-throughput, automated image analysis protocol for the capture, identification and analysis of thermal images acquired with a long-wave infrared (IR) camera was developed to quantify the osmotic stress response of wheat and barley to salinity. There was a strong curvilinear relationship between direct measurements of stomatal conductance and leaf temperature of barley grown in a range of salt concentrations. This indicated that thermography accurately reflected the physiological status of salt-stressed barley seedlings. Leaf temperature differences between barley grown at 200 mM NaCl and 0 mM NaCl reached 1.6°C – the sensitivity of the IR signal increasing at higher salt concentrations. Seventeen durum wheat genotypes and one barley genotype, known to vary for osmotic stress tolerance, were grown in control (no salt) and 150 mM NaCl treatments to validate the newly-developed automated thermal imaging protocol. The ranking of the 18 genotypes based on both a growth study and the IR measurements was consistent with previous reports in the literature for these genotypes. This study shows the potential of IR thermal imaging for the screening of large numbers of genotypes varying for stomatal traits, specifically those related to salt tolerance.

In C₄ plants, acclimation to growth at low irradiance by means of anatomical and biochemical changes to leaf tissue is considered to be limited by the need for a close interaction and coordination between bundle sheath and mesophyll cells. Here differences in relative growth rate (RGR), gas exchange, carbon isotope discrimination, photosynthetic enzyme activity, and leaf anatomy in the C₄ dicot Flaveria bidentis grown at a low (LI; 150 μmol quanta m² s⁻¹) and medium (MI; 500 μmol quanta m² s⁻¹) irradiance and with a 12 h photoperiod over 36 d were examined. RGRs measured using a 3D non-destructive imaging technique were consistently higher in MI plants. Rates of CO₂ assimilation per leaf area measured at 1500 μmmol quanta m² s⁻¹ were higher for MI than LI plants but did not differ on a mass basis. LI plants had lower Rubisco and phosphoenolpyruvate carboxylase activities and chlorophyll content on a leaf area basis. Bundle sheath leakiness of CO₂ (ϕ) calculated from real-time carbon isotope discrimination was similar for MI and LI plants at high irradiance. ϕ increased at lower irradiances, but more so in MI plants, reflecting acclimation to low growth irradiance. Leaf thickness and vein density were greater in MI plants, and mesophyll surface area exposed to intercellular airspace (S_m) and bundle sheath surface area per unit leaf area (S_b) measured from leaf cross-sections were also both significantly greater in MI compared with LI leaves. Both mesophyll and bundle sheath conductance to CO₂ diffusion were greater in MI compared with LI plants. Despite being a C₄ species, F. bidentis is very plastic with respect to growth irradiance.