Wheatear Research Articles

Radiotracer and radiation application for rapid measurement of contribution of stem assimilates towards grain filling and for alleviating terminal heat stress in wheat

Contribution of stem photoassimilates towards grain filling may have significance under stress, since it may force the plant to initiate an early leaf senescence, thereby limiting the availability of flag leaf photosynthates for grain filling. Radiotracer feeding of 14C-sucrose to the internode by stem injection method at 20 days stage after anthesis and measurement of 14C activity in grains at 5 days after foliar application of potassium iodide, meant to inhibit current photosynthesis by inducing leaf senescence, showed a gradual decline in flag leaf chlorophyll but an increase in accumulation of 14C particularly in the basal spikelet grains. However, a reduction in grain filling at all the sink positions within the wheat ear was measured with hastened leaf senescence. On the other hand, abiotic stresses such as high temperature stress, is known to cause an abortion of the basal florets in wheat and induce an early flag leaf senescence. We have earlier shown that seed gamma irradiation, at 0.1 kGy causes an increase in grain mass at middle and basal spikelet positions. In this paper we report that the reduction in grain yield in response to terminal heat stress can be compensated by gamma irradiation, owing to better stem reserve mobilization into the basal and middle spikelet under condition of stress induced senescence.

The productivity elements of the main ear of soft winter wheat in the conditions of the forest-steppe of the Middle Volga region

Research has been conducted on forming the productivity elements of main ear of soft winter wheat in the conditions of the forest-steppe of the middle Volga region. It was found that the productivity of main ear is in a strong positive correlative dependence on a number of seeds per ear and on the mass of 1000 grains. The other elements of the structure were also examined in detail – the ear length, the number of spikelets per ear, intravarietal and intervarietal variability of them.

Effects of Low Temperature at Booting Stage on Sucrose Metabolism and Endogenous Hormone Contents in Winter Wheat Spikelet.

Low spring temperatures often occur during the winter wheat booting stage, when the young ears are very sensitive to cold. In this study, we used two wheat varieties differing in cold sensitivity (sensitive variety Yangmai 18 and tolerant variety Yannong 19) to examine the effect of low temperature on wheat grain number at booting stage. Low temperature stress was simulated in an artificial climate chamber at 4°C for 60 h in 2016 and at 2, 0, or −2°C for 24 h in morphological assays, showing that the development of wheat spikelets was inhibited and floret growth was delayed following low temperature stress. However, an increase in the sucrose content of young panicles was also observed, and the activity of enzymes involved in sucrose metabolism was dynamically altered. Sucrose phosphate synthase activity was enhanced, and sucrose synthase activity significantly increased after treatment at 4 and 2°C, respectively. However, activities of sucrose synthase and invertase decreased with a reduction in temperature. Gene expression assays further revealed downregulation of TaSuS1 expression and upregulation of TaSuS2, while expression of CWINV was inhibited. Moreover, phytohormone content assays showed an increase in the content of abscisic acid in young wheat ears, but a decrease in the content of auxin and gibberellins. The grain number per spike and 1000-grain weight also showed a downward trend following low temperature stress. Overall, these findings suggest that low temperature at booting induces abscisic acid accumulation in winter wheat, altering the activity of the enzymes involved in sucrose metabolism, which leads to an accumulation of sucrose in the young ears, thereby having a negative effect on wheat production.

Evaluation of the electrochemical performance of electrospun transition metal oxide-based electrode nanomaterials for water CDI applications

Composite fibrous materials based on (graphene-enriched) nitrogen-doped carbon/transition metal oxides were produced by electrospinning and their physicochemical properties were thoroughly investigated by a combination of characterisation techniques. The electrochemical behaviour of the electrodes prepared with them was evaluated in view of their use in the capacitive deionisation of saline water. The morphology of the materials reminded of usnea florida lichens, wheat ears, sea sponges and noodles and depended on the transition metal (Mn, Fe, Ti or Zn). The morphology and the relative amount (14.1–22.2 wt%) of the surface nitrogen and carbon-bonded oxygen functional species, beneficial to wettability and involving pseudocapacitive processes, had strong impact on the specific capacitance (43.7–67.4 F g−1, at 5 m V s−1 scan rate), whereas also the specific micropore volume (0.4–5.6 mm3 g−1) affected the effective areal capacitance of the electrodes (1.2–6.0 F m−2, at 5 mV s−1). Ion storage in the composite materials occurred via a mixed capacitive/pseudocapacitive process. Hence, increasing the content of the oxide (from 24.6 to 56.7 wt%), thanks to the fast-reversible redox reactions at or near surface it involves, partly compensated for the growing hindrance to diffusion encountered by the ions (hampered electrostatic adsorption) as the scan rate increased from 5 to 100 mV s−1.

Smart method of agricultural drought regionalization: A winter wheat case study

Abstract The occurrence of drought during flowering (usually from the end of May to the beginning of June) is the most hazardous timing in terms of the possible negative impact of agricultural drought on winter wheat, which is the most cultivated crop in the Czech Republic (about 800000 ha). Lack of water, often accompanied by high temperatures, negatively affects the number of grains in the wheat ear and the tissue development of the developing grain, with consequent impacts on yield and quality of product. With the use of a) long-term time series of agrometeorological data (1961–2010), b) long-term phenological time series of winter wheat (1981–2010), and c) soil conditions data (available water capacity of soils of the Czech Republic) for the arable soil, the ratio of actual evapotranspiration and potential evapotranspiration for the period of 1961–2010, used as an indicator of agricultural drought (lack of water) for wheat, was calculated. The innovative aspect of this categorization of the territory of the Czech Republic according to the risk of occurrence of agricultural drought for winter wheat is considering drought from the aspect of the plant, i.e., evaluation based on the actual consumption of water by the vegetation. This is a very sophisticated procedure. Frequently, water content in soils data, presented as an output of some models, do not fully indicate the possible negative impacts on yield generation because the plants themselves are typically not considered. The method used in this study is universally applicable and allows comparisons of regions at the local, regional, and supra-regional levels. For estimation of the development of agronomic drought in the future, the basic water balances in the growing seasons of 1961–2010 and 2071–2100 were compared using a climate scenario. The forecast indicates a significant deterioration of agricultural drought in the region with probable direct impacts on agricultural production.

Combined Use of FCN and Harris Corner Detection for Counting Wheat Ears in Field Conditions

Accurate counting of wheat ears in field conditions is vital to predict yield and for crop breeding. To quickly and accurately obtain the number of wheat ears in a field, we propose herein a method to count wheat ears based on fully convolutional network (FCN) and Harris corner detection. The technical procedure consists essentially of 1) constructing a dataset of wheat-ear images from acquired red-green-blue (RGB) images; 2) training a FCN as the wheat-ear segmentation model by using the constructed image dataset; 3) preparing testing images and inputting them into the segmentation model to get the initial segmentation results; 4) binarizing the initial segmentation by using the Otsu algorithm (to facilitate subsequent processing); and 5) applying Harris corner detection after extracting the wheat-ear skeleton to obtain the number of wheat ears in the images. The segmentation results show that the proposed FCN-based segmentation model segments wheat ears with an average accuracy of 0.984 and at low computational cost. An average of only 0.033 s is required to segment a 256× 256 -pixel wheat-ear image. Moreover, the segmentation result is improved by nearly 10% compared with the previous segmentation methods under conditions of wheat-ear occlusion, leaf occlusion, uneven illumination, and soil disturbance. Subsequently, the proposed counting method achieves good results, with an average accuracy of 0.974, a coefficient of determination (R 2 ) of 0.983, and a root mean square error (RMSE) of 14.043. These metrics are all improved by 10% compared with the previous methods. These results show that the proposed method accurately counts wheat ears even under conditions of wheat-ear adhesion. Furthermore, the results provide an important technique for studying wheat phenotyping.

Self-adversarial Training and Attention for Multi-task Wheat Phenotyping

Abstract. Phenotypic monitoring provides important data support for precision agriculture management. This study proposes a deep learning-based method to gain an accurate count of wheat ears and spikelets. The deep learning networks incorporate self-adversarial training and attention mechanism with stacked hourglass networks. Four stacked hourglass networks follow a holistic attention map to construct a generator of self-adversarial networks. The holistic attention maps enable the networks to focus on the overall consistency of the whole wheat. The discriminator of self-adversarial networks displays the same structure as the generator, which causes adversarial loss to the generator. This process improves the generator’s learning ability and prediction accuracy for occluded wheat ears. This method yields higher wheat ear count in the Annotated Crop Image Database (ACID) data set than the previous state-of-the-art algorithm. Keywords: Attention mechanism, Plant phenotype, Self-adversarial networks, Stacked hourglass.

Different grain-filling rates explain grain-weight differences along the wheat ear.

Thousand grain weight is one of the components determining wheat grain yield. It represents the average value of individual grain weights which depends on position within the ear and on positon within the spikelet. Our objective was to quantify the influences of individual floret anthesis date, of carpel weight at anthesis and of rate and duration of grain filling, on variation in individual final grain weight. Two bread wheat cultivars were grown in a greenhouse and their ears were sampled from anthesis through to harvest. Each ear was divided into three parts—basal, central and apical—where the two proximal grains were dissected from each of two spikelets. We analysed (i) the flowering time shift within the ear and within the spikelet; and (ii) the growth kinetics during grain filling in relation to position along the ear. For both cultivars, florets located in the central part of the ear were the first to reach anthesis followed by those in the apical part and then the basal part. Within a spikelet, the floret located nearest the rachis flowered first followed by the more distal ones. We found no significant systematic effect of flowering time-shift on final grain weight. Nevertheless, grains in the central part were heavier than the basal ones (9.75% smaller) and than the apical ones (18.25% smaller). These differences were explained mainly by differences in mean grain filling rates. Analysis of growth kinetics enabled an improved explanation of the variability of individual grain weight along the ear.

ESEM-EDS-based analytical approach to assess nanoparticles for food safety and environmental control

Particulate contamination in the form of nanoparticles (NPs) can occur not only in the environment but also in the food production chain, making human exposure almost inevitable. In the present study the potentialities of Environmental Scanning Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (ESEM-EDS) were exploited for the detection and visualization of inorganic NPs in air, raw materials and food products along the pasta production chain (wheat ear, wheat, semolina and pasta). Investigation of the elemental composition of NPs in the same samples was also carried out by using two independent techniques: ESEM-EDS and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). As for airborne particles, size-fractionated sampling was performed by an eight-stage cascade impactor located near the production plant with different exposition times for both ESEM-EDS and gravimetric analysis, respectively. In the case of raw materials and food products, the particles were collected on polycarbonate filters after immersion in milli-Q water in order to recover the NPs deriving from external environmental contamination. Only particle sizes lower than 0.8 µm were taken into consideration when ESEM-EDS analysis was carried out. NPs containing mainly Fe and Ti having dimensions <0.15 µm were identified in the filters used for wheat ear, wheat, semolina and pasta samples. As for air monitoring, gravimetric analysis showed an increase in the concentration levels of total particles during winter, particularly in the case of fine particulate matter (PM2.5). ESEM-EDS analysis, performed on filters of the VII and final stages, evidenced the same trend observed by gravimetric analysis, the majority of Fe-containing NPs being detected.

Assessment of spring wheat varieties by the productivity elements in the Forest Steppe of Ukraine

The article highlights the problems of forming the economically valuable features which affect the productivity of soft spring wheat varieties. The structural analysis data reveal that the number of spikelets in the investigated varieties of wheat ears ranged within 15.4 pcs. In the Triso variety to 20.1 pcs. in the Kharkivska 30, the rate for the variety standard was registered 16.0. The variability amplitude rate varied from 2.0 spikelets in Hordynya and Simkoda Myronivska varieties to 5.0 in the Kharkivska 30 and standart Elhiya Myronivska varieties. The variability range of spikelets number in an ear in the Hordynya, Simkoda Myronivska and Speranza varieties and the average one in all the other varieties was revealed. It was established that grains number in soft spring wheat varieties ranged from 41.1 grains in the Tria variety to 52.6 in the Hordynya variety, in the standard variety of Elehiya Myronivska the rate leveled 36.3. Thus, in all the studied varieties the number of grains in an ear exceeded the standard. The variability of grains number in an ear was significant in the Speranza variety, while in other varieties as well as in the standard sort of Elehiya Myronivska it was medium. It was shown that an ear grains weight in the varieties ranged from of 1.6 g in the Triso variety to 2.2 g in the Hordynya variety. This feature varied in the varieties by the variability range. The largest (1.1 g) it was in the the Hordynya and Speranza varieties, the smallest (0.5 g) – in the variety of Simkoda Myronivska. The variation factor proved significant variability of the trait in the Trizo and Speranza varieties and in the variety standard. All the other studied varieties were the average variability of grain weight in an ear. It was found that all but wheat varieties of soft spring wheat had high indices of 1000 seeds and this trait variation was insignificant indicated by the variation ratewhich was below 10 %. The correlation of crop yield and the basic economically valuable traits are analyzed; positive and negative correlations are established. It was noted that the crop yield of the studied soft spring wheat varieties correlates most closely with the number of grain in an ear (r = 0.77 ± 0.06) and the number of spikelets in an ear (r = 0.61 ± 0.07), and therefore it is necessary to pay attention to these data uppermost. Thus, the study of economically valuable traits of modern varieties of soft spring wheat reveals different variability nature and correlation degrees which enables to define the sources of economically valuable traits in the studied varieties and to further use them in research and selection programs as valuable parent material. Key words: spring wheat, varieties, productivity, ears number, grains number, grain weight, weight of 1000 seeds, correlation.

Ear density estimation from high resolution RGB imagery using deep learning technique

Wheat ear density estimation is an appealing trait for plant breeders. Current manual counting is tedious and inefficient. In this study we investigated the potential of convolutional neural networks (CNNs) to provide accurate ear density using nadir high spatial resolution RGB images. Two different approaches were investigated, either using the Faster-RCNN state-of-the-art object detector or with the TasselNet local count regression network. Both approaches performed very well (rRMSE≈6%) when applied over the same conditions as those prevailing for the calibration of the models. However, Faster-RCNN was more robust when applied to a dataset acquired at a later stage with ears and background showing a different aspect because of the higher maturity of the plants. Optimal spatial resolution for Faster-RCNN was around 0.3 mm allowing to acquire RGB images from a UAV platform for high-throughput phenotyping of large experiments. Comparison of the estimated ear density with in-situ manual counting shows reasonable agreement considering the relatively small sampling area used for both methods. Faster-RCNN and in-situ counting had high and similar heritability (H²≈85%), demonstrating that ear density derived from high resolution RGB imagery could replace the traditional counting method.

The distribution of mycotoxins in a heterogeneous wheat field in relation to microclimate, fungal and bacterial abundance.

To observe the variation in accumulation of Fusarium and Alternaria mycotoxins across a topographically heterogeneous field and tested biotic (fungal and bacterial abundance) and abiotic (microclimate) parameters as explanatory variables. We selected a wheat field characterized by a diversified topography, to be responsible for variations in productivity and in canopy-driven microclimate. Fusarium and Alternaria mycotoxins where quantified in wheat ears at three sampling dates between flowering and harvest at 40 points. Tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), deoxynivalenol (DON), zearalenone (ZEN) and deoxynivalenol-3-Glucoside (DON.3G) were quantified. In canopy temperature, air and soil humidity were recorded for each point with data-loggers. Fusarium spp. as trichothecene producers, Alternaria spp. and fungal abundances were assessed using qPCR. Pseudomonas fluorescens bacteria were quantified with a culture based method. We only found DON, DON.3G, TeA and TEN to be ubiquitous across the whole field, while AME, AOH and ZEN were only occasionally detected. Fusarium was more abundant in spots with high soil humidity, while Alternaria in warmer and drier spots. Mycotoxins correlated differently to the observed explanatory variables: positive correlations between DON accumulation, tri 5 gene and Fusarium abundance were clearly detected. The correlations among the others observed variables, such as microclimatic conditions, varied among the sampling dates. The results of statistical model identification do not exclude that species coexistence could influence mycotoxin production. Fusarium and Alternaria mycotoxins accumulation varies heavily across the field and the sampling dates, providing the realism of landscape-scale studies. Mycotoxin concentrations appear to be partially explained by biotic and abiotic variables. We provide a useful experimental design and useful data for understanding the dynamics of mycotoxin biosynthesis in wheat.

Antagonistic Potential of Fluorescent Pseudomonads Colonizing Wheat Heads Against Mycotoxin Producing Alternaria and Fusaria.

Natural control of phytopathogenic microorganisms is assumed as a priority function of the commensal plant microbiota. In this study, the suitability of fluorescent pseudomonads in the phyllosphere of crop plants as natural control agents was evaluated. Under field conditions, ears of winter wheat were found to be colonized with high consistency and at a high density by pseudomonads at the late milk dough stage. Isolates of these bacteria were evaluated for their potential to protect the plants from phytopathogenic Alternaria and Fusarium fungi. More Pseudomonas isolates were antagonistically active against alternaria than against fusaria in the dual culture test. The alternaria responded species-specifically and more sensitively to bacterial antagonism than the strain-specific reacting fusaria. A total of 110 randomly selected Pseudomonas isolates were screened for genes involved in the biosynthesis of the antibiotics 2,4-diacetylphloroglucinol, phenazine-1-carboxylic acid, pyoluteorin, and pyrrolnitrin. The key gene for production of the phloroglucinol was found in none of these isolates. At least one of the genes, encoding the biosynthesis of the other antibiotics was detected in 81% of the isolates tested. However, the antagonistic effect found in the dual culture assay was not necessarily associated with the presence of these antibiotic genes. Wheat grains as natural substrate were inoculated with selected antagonistic Pseudomonas isolates and Alternaria and Fusarium strains, respectively. The fungal growth was only slightly delayed, but the mycotoxin production was significantly reduced in most of these approaches. In conclusion, the distribution of phytopathogenic fungi of the genera Alternaria and Fusarium in the field is unlikely to be inhibited by naturally occurring pseudomonads, also because the bacterial antagonists were not evenly distributed in the field. However, pseudomonads can reduce the production of Alternaria and Fusarium mycotoxins in wheat grains and thus have the potential to improve the crop quality.

Wheat ear recognizing algorithm for high throughput wheat phenotyping

Wheat ear recognizing algorithm for high throughput wheat phenotyping

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM.

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

Controlling Preharvest Sprouting of Wheat through Nanonetworks

To address the severe problem of preharvest sprouting of wheat, we fabricated and tested a nanonetwork-structured nanocomposite using palygorskite modified by amino silicon oil. The nanocomposite was used as a nanonet membrane (NNM) on the surface of wheat seeds to effectively inhibit preharvest sprouting (PHS). The NNM made the surface of the wheat seeds rougher and formed a hydrophobic nanomembrane on the surface of the wheat ears, effectively preventing the wheat seeds from adsorbing water. The PHS ratio of wheat decreased by 51.4% in the experimental group compared to the control. This study provides a promising approach to prevent wheat preharvest sprouting, which has potential value for application to wheat production.

The influence of the properties of materials used to make the working bodies of the stripper header on the quality of the performed process

The aim of the inveatigations is to develop the main provisions of the procedure for determining the influence of material properties on the example of steel and ultra-high molecular weight polyethylene (UHMW PE) when they are used to fabricate or cover the outer and inner surfaces of the fairing and the combing teeth of the header, on the quality of the process performed by it. The dependencies and regularities that are integral parts of the previously developed mathematical model of the process of combing grain crops with a single-barrel header are used in the article. As a result of solving the equations, data on the normal and tangential components of the speed of movement of the ear and grain after contact with the test surfaces were obtained. Upon contact with the external surface of the fairing that material would be preferred which the movement along is accompanied by a lower value of the frictional force. During a rebound from the combing tooth and the inner surface of the fairing, the best material is the speed of rebound of the grain from which is higher. The value of the normal forces pressing the ear of winter wheat to the contacting surface was determined in an experimental plant simulating the process of combing the reaper. The coefficient of static friction and motion and the coefficient of recovery upon contact with the surfaces of steel and UHMW PE ears and winter wheat’s grains were determined at its moisture content of 9 %. It has been established that at a header speed of 3 m/s, the frictional force pressing the ear to the outer surface of the fairing from UHMW PE is 1,75 times less than on the steel. After contact with the tooth, the rebound speed of the grain is higher, if it is made of steel. For the accepted characteristics of the harvested plants and the operating conditions of the header, when the grains come into contact with the inner surface of the fairing from the UHMW PE within the friction angle, the loss of grain velocity after the collision is less than on the steel, with the exception of contact with direct impact. Compensation for the reduction in speed within a direct impact on the inner surface of the fairing which is made of UHMW PE can be achieved by varying the angle of inclination of the combing teeth on the drum.

Durum wheat ears perform better than the flag leaves under water stress: Gene expression and physiological evidence

Drought is the main abiotic stress threatening wheat production in the Mediterranean region. While the negative effect of drought on the photosynthetic carbon and nitrogen metabolism of the flag leaf has been widely studied, little is known about its effect on other photosynthetic organs such as the ear. Our study compared the responses to water stress of organ temperature, spectral vegetation indices, nitrogen content, carbon isotope composition (δ13C) and expression of key genes for primary metabolism and drought-stress response in the flag leaf and the ear. Measurements were performed at heading and early grain filling in field-grown durum wheat under irrigated and rainfed conditions. Multivariate analysis of physiological traits and gene expression indicated that ears had a similar behaviour regardless of the water regime, while water stress led to significant negative effects on flag leaves. This better performance of ears under water stress compared to leaves was due to good nitrogen and water status and higher expression of key genes for primary metabolism and drought-stress responses, which also indicate a pattern of delayed senescence in ears. Upregulation of genes involved in respiration, CO2 refixation and nitrogen assimilation in ears may also suggest the relevance of these processes in ear metabolism under water stress. This study highlights the importance of including ear traits when unravelling the mechanisms that facilitate adaption of wheat to future environmental scenarios.

SpikeDroidDB: AN INFORMATION SYSTEM FOR ANNOTATION OF MORPHOMETRIC CHARACTERISTICS OF WHEAT SPIKE

The structure of the ear is one of the most important features of cereals associated with such agronomically important traits as productivity, resistance to environmental factors and pests, threshebility. Ears differ in shape, size, density, awnedness, color, etc. Analysis of the ear traits requires visual inspection, manual measurements and is very time-consuming. The effectiveness of ears’ phenotyping can be improved by the introduction of an automated image processing technology, storage of information in databases, use of machine learning algorithms to analyze this information. This paper presents a new approach for collecting, storing and analyzing of information about morphometric characteristics of ears of wheat. Two protocols for obtaining digital images of the ear have been developed. The computer-aided information system SpikeDroidDB has been developed, which allows you to store digital images of the ear, annotate their phenotypic features (14 features, including plant variety description, links to parent genotypes, generation, planting number, ear morphology description). The interface provides a flexible query system to access the data. SpikeDroidDB represents an interconnected representation between genotype, phenotype, location, and growing conditions. The web interface of SpikeDroidDB is available at http://spikedroid.biores . cytogen.ru/ and allows you to work with the system as with desktop computers or mobile devices. We used SpikeDroidDB for the digitization and annotation of a collection of ears of F 2 hybrids from crosses between the Australian cultivar of common wheat Triple Dirk and accession KU506 of Chinese wheat Triticum yunnanense. This experiment includes analysis of 104 plants, 230 spike images. The analysis of the variability of ears in form, length, and other traits allowed determination of the type of their genetic control: compactness is controlled by two recessive genes, awn type and hairi ness at the site of attachment of the spikelet to the axis is controlled by single dominant gene type, hairiness on the axis of the spike is controlled by two dominant genes.

Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images

BackgroundThe number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image.ResultsThe results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears.ConclusionsDeveloping robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.