Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging.

Durum wheat is an important cereal that is widely grown in the Mediterranean basin. In addition to high yield, grain quality traits are of high importance for farmers. The strong influence of climatic conditions makes the improvement of grain quality traits, like protein content, vitreousness, and test weight, a challenging task. Evaluation of quality traits post-harvest is time- and labor-intensive and requires expensive equipment, such as near-infrared spectroscopes or hyperspectral imagers. Predicting not only yield but also important quality traits in the field before harvest is of high value for breeders aiming to optimize resource allocation. Implementation of efficient approaches for trait prediction, such as the use of high-resolution spectral data acquired by a multispectral camera mounted on unmanned aerial vehicles (UAVs), needs to be explored. In this study, we have acquired multispectral image data with an 11-band multispectral camera mounted on a UAV and analyzed the data with machine learning (ML) models to predict grain yield and important quality traits in breeding micro-plots. Combining 11-band multispectral data for 34 cultivars and 16 environments allowed to develop ML models with good prediction capability. Applying the trained models to test sets explained a considerable degree of phenotypic variance with good accuracy showing r squared values of 0.84, 0.69, 0.64, and 0.61 and normalized root mean squared errors of 0.17, 0.07, 0.14, and 0.03 for grain yield, protein content, vitreousness, and test weight, respectively.

Crop phenotyping in a context of global change: What to measure and how to do it.

High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies.

Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data.

Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water, and nitrogen-protein contents, structural elements, and the leaf area index. Even so, hyperspectral data could be more extensively exploited to overcome the breeding challenges being faced under global climate change by advancing high-throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full-range reflectance spectra (visible (VIS)-near-infrared (NIR)-short wave infrared (SWIR)) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC-MS metabolite profiles were analyzed in the flag leaves, ear bracts, glumes, and lemmas. The results from regression models exceeded 50% of the explained variation (adj-R2 in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo-inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); γ-aminobutyric acid (GABA) in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS organ spectra and yield prediction are discussed. We feel that this technique will likely to be of interest due to its broad applicability in ecophysiology research, plant breeding programmes, and the agri-food industry.

Regulation of length and density of Arabidopsis root hairs by ammonium and nitrate.

Root hairs expand the effective root surface to increase the uptake of nutrients and water from the soil. Here the local effects of the two major nitrogen sources, ammonium and nitrate, on root hairs were investigated using split plates. In three contrasting accessions of A. thaliana, namely Col-0, Tsu-0 and Sha, root hairs were differentially affected by the nitrogen forms and their concentration. Root hairs in Sha were short in the absence of nitrate. In Col-0, hair length was moderately decreased with increasing nitrate or ammonium. In all accessions, the root hair density was insensitive to 1,000-fold changes in the ammonium concentrations, when supplied locally as the exclusive nitrogen form. In contrast, the root hair density generally increased with nitrate as the exclusive local nitrogen source. The nitrate sensitivity was reduced at mM concentrations in a loss-of-function mutant of the nitrate transporter and sensor gene NRT1;1 (NPF6.3). Little differences with respect to ammonium were found in a mutant lacking four high affinity AMT-type ammonium transporters, but interestingly, the response to high nitrate was reduced and may indicate a general defect in nitrogen signaling in that mutant. Genetic diversity and the presence of the nitrogen transceptor NRT1;1 explain heterogeneity in the responses of root hairs to different nitrogen forms in Arabidopsis accessions.

Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits.

An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.

Metabolome Profiling Supports the Key Role of the Spike in Wheat Yield Performance.

Although the relevance of spike bracts in stress acclimation and contribution to wheat yield was recently revealed, the metabolome of this organ and its response to water stress is still unknown. The metabolite profiles of flag leaves, glumes and lemmas were characterized under contrasting field water regimes in five durum wheat cultivars. Water conditions during growth were characterized through spectral vegetation indices, canopy temperature and isotope composition. Spike bracts exhibited better coordination of carbon and nitrogen metabolisms than the flag leaves in terms of photorespiration, nitrogen assimilation and respiration paths. This coordination facilitated an accumulation of organic and amino acids in spike bracts, especially under water stress. The metabolomic response to water stress also involved an accumulation of antioxidant and drought tolerance related sugars, particularly in the spikes. Furthermore, certain cell wall, respiratory and protective metabolites were associated with genotypic outperformance and yield stability. In addition, grain yield was strongly predicted by leaf and spike bracts metabolomes independently. This study supports the role of the spike as a key organ during wheat grain filling, particularly under stress conditions and provides relevant information to explore new ways to improve wheat productivity including potential biomarkers for yield prediction.

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images.

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

Identification of QTL conferring resistance to stripe rust (Puccinia striiformis f. sp. hordei) and leaf rust (Puccinia hordei) in barley using nested association mapping (NAM).

The biotrophic rust fungi Puccinia hordei and Puccinia striiformis are important barley pathogens with the potential to cause high yield losses through an epidemic spread. The identification of QTL conferring resistance to these pathogens is the basis for targeted breeding approaches aiming to improve stripe rust and leaf rust resistance of modern cultivars. Exploiting the allelic richness of wild barley accessions proved to be a valuable tool to broaden the genetic base of resistance of barley cultivars. In this study, SNP-based nested association mapping (NAM) was performed to map stripe rust and leaf rust resistance QTL in the barley NAM population HEB-25, comprising 1,420 lines derived from BC1S3 generation. By scoring the percentage of infected leaf area, followed by calculation of the area under the disease progress curve and the average ordinate during a two-year field trial, a large variability of resistance across and within HEB-25 families was observed. NAM based on 5,715 informative SNPs resulted in the identification of twelve and eleven robust QTL for resistance against stripe rust and leaf rust, respectively. Out of these, eight QTL for stripe rust and two QTL for leaf rust are considered novel showing no overlap with previously reported resistance QTL. Overall, resistance to both pathogens in HEB-25 is most likely due to the accumulation of numerous small effect loci. In addition, the NAM results indicate that the 25 wild donor QTL alleles present in HEB-25 strongly differ in regard to their individual effect on rust resistance. In future, the NAM concept will allow to select and combine individual wild barley alleles from different HEB parents to increase rust resistance in barley. The HEB-25 results will support to unravel the genetic basis of rust resistance in barley, and to improve resistance against stripe rust and leaf rust of modern barley cultivars.

A nested association mapping population identifies multiple small effect QTL conferring resistance against net blotch (Pyrenophora teres f. teres) in wild barley.

The net form of net blotch caused by the necrotrophic fungus Pyrenophora teres f. teres is a major disease of barley, causing high yield losses and reduced malting and feed quality. Exploiting the allelic richness of wild barley proved to be a valuable tool to broaden the genetic base of resistance of modern elite cultivars. In this study, a SNP-based nested association mapping (NAM) study was conducted to map QTL for P. teres resistance in the barley population HEB-25 comprising 1,420 lines derived from BC1S3 generation. By scoring the percentage of infected leaf area followed by calculation of the average ordinate (AO) and scoring of the reaction type (RT) in two-year field trials a large variability of net blotch resistance across and within families of HEB-25 was observed. Genotype response to net blotch infection showed a range of 48.2% for AO (0.9-49.1%) and 6.4 for RT (2.2-8.6). NAM based on 5,715 informative SNPs resulted in the identification of 24 QTL for resistance against net blotch. Out of these, six QTL are considered novel showing no correspondence to previously reported QTL for net blotch resistance. Overall, variation of net blotch resistance in HEB-25 turned out to be controlled by small effect QTL. Results indicate the presence of alleles in HEB-25 differing in their effect on net blotch resistance. Results provide valuable information regarding the genetic architecture of the complex barley-P. teres f. teres interaction as well as for the improvement of net blotch resistance of elite barley cultivars.