High-throughput Phenotyping Techniques Research Articles

Responses of grapevine rootstocks to drought through altered root system architecture and root transcriptomic regulations

Roots are the major interface between the plant and various stress factors in the soil environment. Alteration of root system architecture (RSA) (root length, spread, number and length of lateral roots) in response to environmental changes is known to be an important strategy for plant adaptation and productivity. In light of ongoing climate changes and global warming predictions, the breeding of drought-tolerant grapevine cultivars is becoming a crucial factor for developing a sustainable viticulture. Root-trait modeling of grapevine rootstock for drought stress scenarios, together with high-throughput phenotyping and genotyping techniques, may provide a valuable background for breeding studies in viticulture. Here, tree grafted grapevine rootstocks (110R, 5BB and 41B) having differential RSA regulations and drought tolerance were investigated to define their drought dependent root characteristics. Root area, root length, ramification and number of root tips reduced less in 110R grafted grapevines compared to 5BB and 41B grafted ones during drought treatment. Root relative water content as well as total carbohydrate and nitrogen content were found to be much higher in the roots of 110R than it was in the roots of other rootstocks under drought. Microarray-based root transcriptome profiling was also conducted on the roots of these rootstocks to identify their gene regulation network behind drought-dependent RSA alterations. Transcriptome analysis revealed totally 2795, 1196 and 1612 differentially expressed transcripts at the severe drought for the roots of 110R, 5BB and 41B, respectively. According to this transcriptomic data, effective root elongation and enlargement performance of 110R were suggested to depend on three transcriptomic regulations. First one is the drought-dependent induction in sugar and protein transporters genes (SWEET and NRT1/PTR) in the roots of 110R to facilitate carbohydrate and nitrogen accumulation. In the roots of the same rootstock, expression increase in osmolyte producer genes revealed another transcriptomic regulation enabling effective root osmotic adjustment under drought stress. The third mechanism was linked to root suberization with upregulation of transcripts functional in wax producing enzymes (Caffeic acid 3-O-methyltransferase, Eceriferum3, 3-ketoacyl-CoAsynthase). These three transcriptomic regulations were suggested to provide essential energy and water preservation to the roots of 110R for its effective RSA regulation under drought. This phenotypic and genotypic knowledge could be used to develop root-dependent drought tolerant grapevines in breeding programs and could facilitate elucidation of genetic regulations behind RSA alteration in other plants.

Improving nitrogen use efficiency in plants: effective phenotyping in conjunction with agronomic and genetic approaches.

For global sustainable food production and environmental benefits, there is an urgent need to improve N use efficiency (NUE) in crop plants. Excessive and inefficient use of N fertiliser results in increased crop production costs and environmental pollution. Therefore, cost-effective strategies such as proper management of the timing and quantity of N fertiliser application, and breeding for better varieties are needed to improve NUE in crops. However, for these efforts to be feasible, high-throughput and reliable phenotyping techniques would be very useful for monitoring N status in planta, as well as to facilitate faster decisions during breeding and selection processes. This review provides an insight into contemporary approaches to phenotyping NUE-related traits and associated challenges. We discuss recent and advanced, sensor- and image-based phenotyping techniques that use a variety of equipment, tools and platforms. The review also elaborates on how high-throughput phenotyping will accelerate efforts for screening large populations of diverse genotypes in controlled environment and field conditions to identify novel genotypes with improved NUE.

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3- to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.

Comparative performance of spectral and thermographic properties of plants and physiological traits for phenotyping salinity tolerance of wheat cultivars under simulated field conditions.

Successful plant breeding in saline environments requires high-throughput phenotyping techniques to differentiate genotypes for salinity tolerance. This study employed advanced, non-destructive sensing technologies to identify traits contributing to salinity tolerance in wheat (Triticum aestivum L.). Plants were grown in large containers to simulate field conditions for control, salinity stress alone, and combined salinity and drought stress treatments. The comparative performance of spectral reflectance sensing, thermography, digital imaging, and the assessment of physiological traits of two wheat cultivars were tested at booting, anthesis and grain filling. Variation in grain yield between the two cultivars was significant for all treatments (controls, P<0.01; others, P<0.001), whereas there were no significant differences in straw DW regardless of treatment. Among the spectral and thermographic assessments, spectral indices were sufficiently sensitive to detect genotypic differences in salinity tolerance among the wheat cultivars after anthesis for the salinity alone and combined treatments. In contrast, physiological traits such as leaf water status and photosynthetic properties demonstrated no differences between the wheat cultivars for either the salinity alone or the combined treatments. These results suggest that spectral sensing has the potential for high-throughput screening of phenotypic traits associated with salinity tolerance of wheat cultivars.

What Should Students in Plant Breeding Know About the Statistical Aspects of Genotype × Environment Interactions?

A good statistical analysis of genotype × environment interactions (G × E) is a key requirement for progress in any breeding program. Data for G × E analyses traditionally come from multi‐environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the importance of a sound statistical framework for analyzing G × E. This paper presents considerations on such a framework from the point of view of the choices that need to be made with respect to the content of short academic courses on statistical methods for G × E. Based on our experiences in teaching statistical methods to plant breeders, for specialized G × E courses between three and 5 d are reserved. The audience in such courses includes MSc students, PhD students, postdocs, and researchers at breeding companies. For such specialized courses, we propose a collection of topics to be covered. Our outlook on G × E analyses is two‐fold. On the one hand, we see the G × E problem as the building of predictive models for genotype‐specific reaction norms. On the other hand, the G × E problem consists in the identification of suitable variance‐covariance models to describe heterogeneity of genetic variance and correlations across environments. Our preferred class of statistical models is the class of mixed linear‐bilinear models. These statistical models allow us to answer breeding questions on adaptation, adaptability, stability, and the identification and subdivision of the target population of environments. By a citation analysis of the literature on G × E, we show that our preference for mixed linear‐bilinear models for analyzing G × E is supported by recent trends in the types of methods for G × E analysis that are most frequently cited.

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.

High-throughput phenotyping (HTP) identifies seedling root traits linked to variation in seed yield and nutrient capture in field-grown oilseed rape (Brassica napus L.).

Background and Aims Root traits can be selected for crop improvement. Techniques such as soil excavations can be used to screen root traits in the field, but are limited to genotypes that are well-adapted to field conditions. The aim of this study was to compare a low-cost, high-throughput root phenotyping (HTP) technique in a controlled environment with field performance, using oilseed rape (OSR; Brassica napus) varieties.Methods Primary root length (PRL), lateral root length and lateral root density (LRD) were measured on 14-d-old seedlings of elite OSR varieties (n = 32) using a ‘pouch and wick’ HTP system (∼40 replicates). Six field experiments were conducted using the same varieties at two UK sites each year for 3 years. Plants were excavated at the 6- to 8-leaf stage for general vigour assessments of roots and shoots in all six experiments, and final seed yield was determined. Leaves were sampled for mineral composition from one of the field experiments.Key Results Seedling PRL in the HTP system correlated with seed yield in four out of six (r = 0·50, 0·50, 0·33, 0·49; P < 0·05) and with emergence in three out of five (r = 0·59, 0·22, 0·49; P < 0·05) field experiments. Seedling LRD correlated positively with leaf concentrations of some minerals, e.g. calcium (r = 0·46; P < 0·01) and zinc (r = 0·58; P < 0·001), but did not correlate with emergence, general early vigour or yield in the field.Conclusions Associations between PRL and field performance are generally related to early vigour. These root traits might therefore be of limited additional selection value, given that vigour can be measured easily on shoots/canopies. In contrast, LRD cannot be assessed easily in the field and, if LRD can improve nutrient uptake, then it may be possible to use HTP systems to screen this trait in both elite and more genetically diverse, non-field-adapted OSR.

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

Zbrowse: an interactive GWAS results browser

The growing number of genotyped populations, the advent of high-throughput phenotyping techniques and the development of GWAS analysis software has rapidly accelerated the number of GWAS experimental results. Candidate gene discovery from these results files is often tedious, involving many manual steps searching for genes in windows around a significant SNP. This problem rapidly becomes more complex when an analyst wishes to compare multiple GWAS studies for pleiotropic or environment specific effects. To this end, we have developed a fast and intuitive interactive browser for the viewing of GWAS results with a focus on an ability to compare results across multiple traits or experiments. The software can easily be run on a desktop computer with software that bioinformaticians are likely already familiar with. Additionally, the software can be hosted or embedded on a server for easy access by anyone with a modern web browser. Subjects Bioinformatics, Computational Biology, Visual Analytics

Advances in crop phenotyping and multi-environment trials

Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques, while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper, we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as green- houses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environ- ments, especially for traits that are difficult to be tested in multi-environment trials (MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.

Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

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.

Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques.

Yield and grain protein concentration (GPC) represent crucial factors in the global agricultural wheat (Triticum aestivum L.) production and are predominantly determined via carbon and nitrogen metabolism, respectively. The maintenance of green leaf area and the onset of senescence (Osen) are expected to be involved in both C and N accumulation and their translocation into grains. The aim of this study was to identify stay-green and early senescence phenotypes in a field experiment of 50 certified winter wheat cultivars and to investigate the relationships among Osen, yield and GPC. Colour measurements on flag leaves were conducted to determine Osen for 20 cultivars and partial least square regression models were used to calculate Osen for the remaining 30 cultivars based on passive spectral reflectance measurements as a high-throughput phenotyping technique for all varieties. Using this method, stay-green and early senescence phenotypes could be clearly differentiated. A significant negative relationship between Osen and grain yield (r2=0.81) was observed. By contrast, GPC showed a significant positive relationship to Osen (r2=0.48). In conclusion, the high-throughput character of our proposed phenotyping method should help improve the detection of such traits in large field trials as well as help us reach a better understanding of the consequences of the timing of senescence on yield.

High-throughput phenotyping early plant vigour of winter wheat

In contrast to high-throughput genotyping which can manage a large number of plants at relatively low cost, phenotyping of many individual genotypes in field trials is still laborious and expensive. Early plant vigour, as an early selection criterion, is a trait that is visually scored due to a lack of suitable phenotyping methods for an accurate detection of this trait in large field trials. A high-throughput phenotyping technique for scoring early plant vigour would enhance the breeding process. This study was conducted to develop a method for scoring phenotypic differences in early plant vigour of 50 winter wheat (Triticum aestivum L.) cultivars in a 2-years experiment using a vehicle based multispectral active sensor and two commercially available active sensors, GreenSeeker and CropCircle. Pixel analysis of RGB images revealed to be the most feasible and superior method compared to other possible reference methods. A comparison between the two years 2011 and 2012 confirmed that early plant vigour was affected by genotypic differences. A novel spectral plant vigour index (EPVI) was found to accurately reflect the plant vigour at tillering. Different methods were applied to identify optimal combinations of wavelengths to predict early plant vigour, including multivariate modelling and prediction, contour maps for identifying all possible simple ratios and testing of combined indices. The EPVI and the relative amount of green pixels (RAGP) derived from digital images were significantly related with r2=0.98 to each other in both years. A total of 200 plots, 12m in length, could be measured within 75min. The EPVI was shown to be an accurate scoring method for the high-throughput screening of large field trials. The rapidity and accuracy of this novel method may contribute to enhanced selection at early growth stages.

Identification of quantitative trait loci controlling fibre length and lignin content in Arabidopsis thaliana stems.

Fibre properties and the biochemical composition of cell walls are important traits in many applications. For example, the lengths of fibres define the strength and quality of paper, and lignin content is a critical parameter for the use of biomass in biofuel production. Identifying genes controlling these traits is comparatively difficult in woody species, because of long generation times and limited amenability to high-resolution genetic mapping. To address this problem, this study mapped quantitative trait loci (QTLs) defining fibre length and lignin content in the Arabidopsis recombinant inbred line population Col-4×Ler-0. Adapting high-throughput phenotyping techniques for both traits for measurements in Arabidopsis inflorescence stems identified significant QTLs for fibre length on chromosomes 2 and 5, as well as one significant QTL affecting lignin content on chromosome 2. For fibre length, total variation within the population was 208% higher than between parental lines and the identified QTLs explained 50.58% of the observed variation. For lignin content, the values were 261 and 26.51%, respectively. Bioinformatics analysis of the associated intervals identified a number of candidate genes for fibre length and lignin content. This study demonstrates that molecular mapping of QTLs pertaining to wood and fibre properties is possible in Arabidopsis, which substantially broadens the use of Arabidopsis as a model species for the functional characterization of plant genes.

High-throughput shoot imaging to study drought responses

Drought is a complex stress which elicits a wide variety of plant responses. As such, genetic studies of drought are particularly difficult. Elucidation of the genetic basis of components contributing to drought tolerance is likely to be more tractable than that of overall drought tolerance. Certain of the traits which contribute to drought tolerance in plants and the high-throughput phenotyping techniques available to measure those traits are described in this paper. On the basis of the dynamic nature of drought, plant development, and the resulting stress response, the focus is on non-destructive imaging techniques which allow a temporal resolution and monitoring of the same plants throughout the experiment. Information on the physiological changes in response to drought over time is vital in order to identify and characterize different drought-tolerance mechanisms. High-throughput imaging provides a valuable new tool which allows the dissection of plant responses to drought into a series of component traits.

Microfluidics-enabled phenotyping, imaging, and screening of multicellular organisms

This paper reviews the technologies that have been invented in the last few years on high-throughput phenotyping, imaging, screening, and related techniques using microfluidics. The review focuses on the technical challenges and how microfluidics can help to solve these existing problems, specifically discussing the applications of microfluidics to multicellular model organisms. The challenges facing this field include handling multicellular organisms in an efficient manner, controlling the microenvironment and precise manipulation of the local conditions to allow the phenotyping, screening, and imaging of the small animals. Not only does microfluidics have the proper length scale for manipulating these biological entities, but automation has also been demonstrated with these systems, and more importantly the ability to deliver stimuli or alter biophysical/biochemical conditions to the biological entities with good spatial and temporal controls. In addition, integration with and interfacing to other hardware/software allows quantitative approaches. We include several successful examples of microfluidics solving these high-throughput problems. The paper also highlights other applications that can be developed in the future.