Wheat Phenotyping Research Articles

TaNAR2.1 and TaNAR2.2 differ in influencing nitrogen uptake and growth of wheat (Triticum aestivum L.)

NAR2 (Nitrate assimilation related protein) is a protein chaperone involved in transporting nitrate across membranes. However, the expression pattern and function of NAR2 genes in wheat are still largely unknown. Here, we cloned two TaNAR2 genes (TaNAR2.1 and TaNAR2.2). To assess and compare the functional differences of TaNAR2.1 and TaNAR2.2, we analyzed the subcellular localization and expression pattern of the two genes in wheat under low nitrogen (LN) and high nitrogen (HN) conditions, as well as the nitrate influx and root system architecture of TaNAR2.1 and TaNAR2.2 overexpression wheat under LN and HN. Additionally, we investigated the effects of TaNAR2.1 and TaNAR2.2 overexpression on the growth phenotype, nitrogen uptake and yield of wheat throughout the growth period. There are significant differences in the expression patterns and functions of TaNAR2.1 and TaNAR2.2. TaNAR2.1 is located in the cytoplasm, nucleus and the plasma membrane, whereas TaNAR2.2 is a cytoplasm-specific protein. TaNAR2.1 appears to exhibit larger changes in expression levels and a higher capacity for nitrate influx than TaNAR2.2 under external nitrate supply. Overexpression of TaNAR2.1 significantly improves grain nitrogen use efficiency and increases grain yield, whereas overexpression of TaNAR2.2 enhances vegetative and reproductive growth of wheat roots. These findings indicate that TaNAR2.1 plays a crucial role in wheat nitrogen accumulation and yield, while TaNAR2.2 is pivotal for wheat root growth.

The TaWAK2-TaNAL1-TaDST pathway regulates leaf width via cytokinin signaling in wheat.

Leaves play a crucial role in photosynthesis and respiration, ultimately affecting the final grain yield of crops, including wheat (Triticum aestivum L.); however, the molecular mechanisms underlying wheat leaf development remain largely unknown. Here, we isolated a narrow-leaf gene, TaWAK2-A, through a map-based cloning strategy. TaWAK2-A encodes a wall-associated kinase (WAK), for which a single Ala-to-Val amino acid substitution reduces the protein stability, leading to a narrow-leaf phenotype in wheat. Further investigation suggests that TaWAK2 directly interacts with and phosphorylates TaNAL1, a trypsin-like serine/cysteine protease. The phosphorylated TaNAL1 is then involved in the degradation of the zinc finger transcription factor TaDST, which acts as a repressor of leaf expansion by activating the expression of the cytokinin oxidase gene TaCKX9 and triggering in vivo cytokinin degradation. Therefore, our findings elucidate a signaling cascade involving TaWAK2-TaNAL1-TaDST that sheds light on the regulation of wheat leaf development.

MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene

Developing a high-precision wheat head detection algorithm is challenging due to the dense distribution and diverse sizes of wheat head in the field, as well as serious coverage from weeds. In this work, we propose multi-scale feature enhancement networks for wheat head detection and counting in complex scene (MFNet). Firstly, we introduce a deformable spatial attention mechanism (DSAM) and embed it in the backbone network to enhance the extraction of wheat head features while suppressing irrelevant features, effectively improving detection of wheat head in occluded environments. Secondly, we design a multi-scale receptive field feature fusion (MRFF) module in combination with an improved light-weight feature pyramid module to achieve more accurate detection of wheat head of different sizes and improve localization accuracy simultaneously. Additionally, the modified detection head with deformable convolution is able to adapt to different shapes of wheat head features and accurately predict the bounding boxes. Our method achieves 94.2% AP@50 at a speed of 30 FPS on the GWHD dataset, and to verify the generalization of the proposed method, we constructed a dense wheat head detection (DWHD) dataset with annotations, conducted experiments on the DWHD and SPIKE datasets and compared them with state-of-the-art algorithms. The experimental results show that the proposed method outperformed most of the existing methods, which further proved the superiority and robustness of the proposed method, and demonstrated that our model possesses excellent adaptability, enabling it to flexibly cope with scenarios of significant scale differences and severe occlusion in complex field environments for wheat head, and provide a technological reference for monitoring the wheat phenotype.

CTHNet: a network for wheat ear counting with local-global features fusion based on hybrid architecture.

Accurate wheat ear counting is one of the key indicators for wheat phenotyping. Convolutional neural network (CNN) algorithms for counting wheat have evolved into sophisticated tools, however because of the limitations of sensory fields, CNN is unable to simulate global context information, which has an impact on counting performance. In this study, we present a hybrid attention network (CTHNet) for wheat ear counting from RGB images that combines local features and global context information. On the one hand, to extract multi-scale local features, a convolutional neural network is built using the Cross Stage Partial framework. On the other hand, to acquire better global context information, tokenized image patches from convolutional neural network feature maps are encoded as input sequences using Pyramid Pooling Transformer. Then, the feature fusion module merges the local features with the global context information to significantly enhance the feature representation. The Global Wheat Head Detection Dataset and Wheat Ear Detection Dataset are used to assess the proposed model. There were 3.40 and 5.21 average absolute errors, respectively. The performance of the proposed model was significantly better than previous studies.

Improving the sustainability of arable cropping systems by modifying root traits: A modelling study for winter wheat

AbstractModifying root systems by crop breeding has been attracting increasing attention as a potentially effective strategy to enhance the sustainability of agriculture by increasing soil organic matter (SOM) stocks and soil quality, whilst maintaining or even improving yields. We used the new soil‐crop model USSF (Uppsala model of Soil Structure and Function) to investigate the potential of this management strategy using winter wheat as a model crop. USSF combines a simple (generic) crop growth model with physics‐based descriptions of soil water flow, water uptake and transpiration by plants. It also includes a model of the interactions between soil structure dynamics and organic matter turnover that considers the effects of physical protection and microbial priming on the decomposition of SOM. The model was first calibrated against field data on soil water contents and both above‐ground and root biomass of winter wheat measured during one growing season in a clay soil in Uppsala, Sweden using the GLUE method to identify five ‘acceptable’ parameter sets. We created four model crops (ideotypes) by modifying root‐related parameters to mimic winter wheat phenotypes with improved root traits. Long‐term (30‐year) simulations of a conventionally tilled monoculture of winter wheat were then performed to evaluate the potential effects of cultivating these ideotypes on the soil water balance, soil organic matter stocks and grain yields. Our results showed that ideotypes with deeper root systems or root systems that are more effective for water uptake increased grain yields by 3% and SOM stocks in the soil profile by ca. 0.4%–0.5% in a 30‐year perspective (as an average of the five parameter sets). An ideotype in which below‐ground allocation of dry matter was increased at the expense of stem growth gave even larger increases in SOM stocks (ca. 1.4%). An ideotype combining all three modifications (deeper and more effective root systems and greater root production) showed even more promising results: compared with the baseline scenario, surface runoff decreased while yields were predicted to increase by ca. 7% and SOM stocks in the soil profile by ca. 2%, which is roughly equivalent to ca. 20% of the 4‐per‐mille target (https://4p1000.org/).

Physiological, biochemical and transcriptomic insights into the mechanisms by which molybdenum mitigates cadmium toxicity in Triticum aestivum L

There are many heavy metal stresses in agricultural biological systems, especially cadmium (Cd) stress, which prevent the full growth of plants, lead to a serious decline in crop yield, and endanger human health. Molybdenum (Mo), an essential nutrient element for plants, regulates plant growth mainly by reducing the absorption of heavy metals and protecting plants from oxidative damage. The aim of this study was to determine the protective effect of Mo (1 μM) application on wheat plants under conditions of Cd (10 μM) toxicity. The biomass, Cd and Mo contents, photosynthesis, leaf and root ultrastructure, antioxidant system, and active oxygen content of the wheat plants were determined. Mo increased the total chlorophyll content of wheat leaves by 43.02% and the net photosynthetic rate by 38.67%, and ameliorated the inhibitory effect of cadmium on photosynthesis by up-regulating photosynthesis-related genes and light-trapping genes. In addition, Mo reduced the content of superoxide anion (O2•—) by 16.55% and 31.12%, malondialdehyde (MDA) by 20.75% and 7.17%, hydrogen peroxide (H2O2) by 24.69% and 8.17%, and electrolyte leakage (EL) by 27.59% and 16.82% in wheat leaves and roots, respectively, and enhanced the antioxidant system to reduce the burst of reactive oxygen species and alleviate the damage of Cd stress on wheat. According to the above results, Mo is considered a plant essential nutrient that enhances Cd tolerance in wheat by limiting the absorption, accumulation and transport of Cd and by regulating antioxidant defence mechanisms. Environmental ImplicationCadmium (Cd)，is one of the most toxic heavy metals in the environment, and Cd pollution is a global environmental problem that threatens food security and human health. Molybdenum (Mo), as an essential plant nutrient, is often used to resist environmental stress. However, the mechanism of Mo treatment on wheat subjected to Cd stress has not been reported. In this study, we systematically analysed the effects of Mo on the phenotype, physiology, biochemistry, ultrastructure and Cd content of wheat subjected to Cd stress, and comprehensively analysed the transcriptomics. It not only reveals the mechanism of Mo tolerance to Cd stress in wheat, but also provides new insights into phytoremediation and plant growth in Cd-contaminated soil.

LHP1-mediated epigenetic buffering of subgenome diversity and defense responses confers genome plasticity and adaptability in allopolyploid wheat

Polyploidization is a major driver of genome diversification and environmental adaptation. However, the merger of different genomes may result in genomic conflicts, raising a major question regarding how genetic diversity is interpreted and regulated to enable environmental plasticity. By analyzing the genome-wide binding of 191 trans-factors in allopolyploid wheat, we identified like heterochromatin protein 1 (LHP1) as a master regulator of subgenome-diversified genes. Transcriptomic and epigenomic analyses of LHP1 mutants reveal its role in buffering the expression of subgenome-diversified defense genes by controlling H3K27me3 homeostasis. Stripe rust infection releases latent subgenomic variations by eliminating H3K27me3-related repression. The simultaneous inactivation of LHP1 homoeologs by CRISPR–Cas9 confers robust stripe rust resistance in wheat seedlings. The conditional repression of subgenome-diversified defenses ensures developmental plasticity to external changes, while also promoting neutral-to-non-neutral selection transitions and adaptive evolution. These findings establish an LHP1-mediated buffering system at the intersection of genotypes, environments, and phenotypes in polyploid wheat. Manipulating the epigenetic buffering capacity offers a tool to harness cryptic subgenomic variations for crop improvement.

Dosage response to reduced height‐1 (Rht‐1) loss‐of‐function mutations and characterization of slender phenotype in hexaploid wheat

AbstractThe reduced height (Rht‐1) genes in wheat (Triticum aestivum L.) are integral in controlling plant height. Previous studies in other plant species have demonstrated that loss‐of‐function mutations in their orthologous Rht‐1 genes results in plants with a slender phenotype illustrated by increased plant heights, sterility, and a constitutive gibberellic acid (GA3) response; however, this phenotype has not been described in wheat. In this study, nonsense alleles occurring in the GRAS domain of Rht‐A1, B1, and D1 were combined to create single, double, and triple Rht‐1 mutants. Homozygous lines possessing none, one, two, or three Rht‐1 stop mutations were grown in replicated field trials in three environments to assess agronomic traits. Germination tests to measure GA3 responsiveness and gene expression analysis via RNA‐seq were also performed. Rht‐1 triple mutants exhibited a slender phenotype characterized by rapid growth, elongated coleoptiles and internodes, elongated spikes, decreased tiller and spikelet number, and sterile heads. The presence of a single functional Rht‐1 gene resulted in a normal phenotype. Differences in plant height among the Rht‐1 double mutants, Rht‐1 single mutants, and Rht‐1 all wild‐type dosages trended toward increased plant height with increased Rht‐1 stop mutation dosage. Differences in Rht‐1 homeolog gene expression did not equate to differences in plant height between the different Rht‐1 stop mutations.

Wheat cis-zeatin-O-glucosyltransferase cZOGT1 interacts with the Ca2+-dependent lipid binding protein TaZIP to regulate senescence.

Premature senescence is an important factor affecting wheat yield and quality. Wheat yield can be increased by delaying senescence and prolonging the effective photosynthetic time. Previously, we found that the cis-zeatin-O-glucosyltransferase (cZOGT1) gene plays an important role in the stay-green wheat phenotype. In this study, cZOGT1-overexpressing lines exhibited a delayed senescence phenotype, despite a significant reduction in the total cytokinin content. Further, we found that cZOGT1 interacted with the Ca2+-dependent lipid binding protein TaZIP (cZOGT1-interacting protein), and that a high level of cZOGT1 expression led to the suppression of TaZIP expression, which in turn, reduced abscisic acid (ABA) content. The synergistic reduction in cytokinins and ABA levels eventually caused the stay-green phenotype in cZOGT1-overexpressing lines. This study provides a new theoretical basis to explain the mechanism underlying the wheat stay-green phenotype and provides a genetic resource for wheat molecular-design breeding.

WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging.

Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model's hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants.

Quantitative Trait Locus (QTL) Mapping for Common Wheat Plant Heights Based on Unmanned Aerial Vehicle Images

The application of unmanned aerial vehicle (UAV) remote sensing technology for high-throughput acquisition of phenotypic values of field plant height is of great significance in plant height monitoring of wheat varieties (lines). Based on the UAV, mounted with high-resolution digital cameras, a low-altitude remote sensing platform was built to obtain images of the Berkut/Worrakatta recombinant inbred lines (RIL) wheat population (297 lines). The optimal digital surface model (DSM) for plant height extraction was constructed by combining the actual wheat plant heights measured in the field. Using a wheat 50K SNP chip, quantitative trait locus (QTL) analysis was performed for field plant height of the RIL population using UAV plant height data to detect the locus related to plant height. We verified the effectiveness of QTL analysis using the UAV plant height data. The results of the DSM plant height extraction method showed that the plant height extraction models during the entire growth period had a highly significant correlation (p < 0.001); the measured plant height correlated with the predicted plant height, with prediction model accuracy R2 = 0.7527 and verification model accuracy R2 = 0.8214. QTL analysis was conducted on the manually and UAV-measured plant height traits. For the manually measured plant height phenotypes, one locus related to plant height was detected on wheat chromosome 6A and explained 13.12% of phenotypic variation. For the UAV-measured phenotypes, one locus related to plant height was also detected on wheat chromosome 6A and explained 9.62% of phenotypic variation. The QPH.xjau-6A locus on chromosome 6A, which featured in the results of the two measurement methods, is a stable locus, indicating that the mapping results obtained using the actual plant height values were in good agreement with those obtained using the UAV extracted values. Three candidate genes related to plant height were screened: they encode protein kinase, NAC domain protein, and cytochrome P450, respectively. Therefore, this study provided reference information for monitoring plant phenotype and growth of wheat and also for the extraction of plant height for wheat breeding in the future.

Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties

The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R2 = 0.61–0.7 and R2 = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R2 = 0.25–0.35 and R2 = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties.

A bifunctional selectable marker for wheat transformation contributes to the characterization of male-sterile phenotype induced by a synthetic Ms2 gene.

An engineered selectable marker combining herbicide resistance and yellow fluorescence contributes to the characterization of male-sterile phenotype in wheat, the severity of which correlates with expression levels of a synthetic Ms2 gene. Genetic transformation of wheat is conducted using selectable markers, such as herbicide and antibiotic resistance genes. Despite their proven effectiveness, they do not provide visual control of the transformation process and transgene status in progeny, which creates uncertainty and prolongs screening procedures. To overcome this limitation, this study developed a fusion protein by combining gene sequences encoding phosphinothricin acetyltransferase and mCitrine fluorescent protein. The fusion gene, introduced into wheat cells by particle bombardment, enabled herbicide selection, and visual identification of primary transformants along with their progeny. This marker was then used to select transgenic plants containing a synthetic Ms2 gene. Ms2 is a dominant gene whose activation in wheat anthers leads to male sterility, but the relationship between the expression levels and the male-sterile phenotype is unknown. The Ms2 gene was driven either by a truncated Ms2 promoter containing a TRIM element or a rice promoter OsLTP6. The expression of these synthetic genes resulted in complete male sterility or partial fertility, respectively. The low-fertility phenotype was characterized by smaller anthers than the wild type, many defective pollen grains, and low seed sets. The reduction in the size of anthers was observed at earlier and later stages of their development. Consistently, Ms2 transcripts were detected in these organs, but their levels were significantly lower than those in completely sterile Ms2TRIM::Ms2 plants. These results suggested that the severity of the male-sterile phenotype was modulated by Ms2 expression levels and that higher levels may be key to activating total male sterility.

QTL analysis identified two major all-internodes solidness loci from a completely solid-stemmed spring wheat line.

The culms of solid-stemmed wheat cultivars are filled with "pith" - a parenchymatous tissue largely composed of soft, spongy, and compact parenchyma cells. Breeding solid-stemmed cultivars is the most effective way to decrease the detrimental impact of wheat stem sawfly (WSS), Cephus cinctus Norton (Hymenoptera: Cephidae) on wheat production. Althougha major solid stem gene has been previously identified from durum wheat, it produces an intermediate level of stem solidness in common wheat which is insufficient to provide the required level of WSS resistance. The maximum resistance is achieved when stems are totally filled with pith. Thus, to identify a secondary source of solidness in common wheat, we developed three mapping populations from wheat cvs. Sadash, 'AAC Innova' and 'AAC Cameron', each crossed separatelywith P2711, a completely solid-stemmed hexaploid wheat breeding line. All populations were genotyped using either wheat 15K or 90K Infinium iSelect SNP Assay and high-density linkage maps were generated from individual populations along with consensus maps for chromosomes 3B and 3D from all populations. 'Sadash/P2711' and 'AAC Innova/P2711' populations were subjected to extensive phenotyping in ≥3 environments followed by quantitative trait loci (QTL) analyses using population-specific and consensus linkage maps. We identified two major solid stem QTLs in the distal regions of chromosome arms 3BL and 3DL in both populations in addition to several population-specific or common minor QTLs. Internode-specific QTL analyses detected both major QTLs of chromosomes 3B and 3D across internodes, from top to bottom of the stalk, but minor QTLs were largely detected in upper or middle internodes. Our results suggest that both major QTLs are sufficient to develop highly solid-stemmed cvs; however, the minor loci, which additively enhance the pith expression, can be coupled with major genes to achieve a complete solid stem phenotype in common wheat. Comparative and haplotype analyses showed that the 3B locus is homoeologous to 3D, the former being mapped to a 1.1 Mb genomic region. Major QTLs identified in this study can be incorporated in modern wheat cultivars to achieve maximum WSS resistance from high pith expression.

Yield-SpikeSegNet: An Extension of SpikeSegNet Deep-Learning Approach for the Yield Estimation in the Wheat Using Visual Images

ABSTRACT High-throughput plant phenotyping integrated with computer vision is an emerging topic in the domain of nondestructive and noninvasive plant breeding. Analysis of the emerging grain spikes and the grain weight or yield estimation in the wheat plant for a huge number of genotypes in a nondestructive way has achieved significant research attention. In this study, we developed a deep learning approach, “Yield-SpikeSegNet,” for the yield estimation in the wheat plant using visual images. Our approach consists of two consecutive modules: “Spike detection module” and “Yield estimation module.” The spike detection module is implemented using a deep encoder-decoder network for spike segmentation and output of this module is spike area and spike count. In yield estimation module, we develop machine learning models using artificial neural network and support vector regression for the yield estimation in the wheat plant. The model’s precision, accuracy, and robustness are found satisfactory in spike segmentation as 0.9982, 0.9987, and 0.9992, respectively. The spike segmentation and yield estimation performance reflect that the Yield-SpikeSegNet approach is a significant step forward in the domain of high-throughput and nondestructive wheat phenotyping.

Key phenotypes related to wheat grain yield in a two‐site multicultivar test

AbstractYield performances of wheat (Triticum aestivum L.) cultivars are decided by the combined effects of key phenotypes. However, it is unclear which phenotypes are the key phenotypes that determine yield. We conducted a two‐site multicultivar test in Henan and Anhui in China during the 2016‒2017 wheat growing season. The objectives were to assess the relationships between grain yield and other related phenotypes and reveal the key phenotypes related to grain yield. Grain yield and nineteen phenotypes were assessed in a field trial consisting of 28 wheat cultivars. Multivariate statistical analysis methods (e.g., principal component analysis, partial least squares model, structural equation model) were used to reveal the relationships between grain yield and other related phenotypes. Wheat phenotypes changed as a result of the differences in environment (site) and genotype (cultivar). Eleven phenotypes were identified as the key phenotypes closely related to grain yield: biomass, harvest index, spike density, leaf nitrate reductase, net photosynthetic rate, canopy temperature, leaf chlorophyll, leaf area index, stomatal conductance, carboxylation efficiency, and transpiration rate. For analyzing the mechanisms of yield differences, multiple related phenotypes (rather than a single phenotype) should be considered.

A dominant gene Ihrl1 is tightly linked to and inhibits the gene Ndhrl1 mediating nitrogen-dependent hypersensitive reaction-like phenotype in wheat.

Identification and mapping of an inhibitor of Ndhrl1 mediating nitrogen-dependent hypersensitive reaction-like phenotype in wheat. Hypersensitive reaction-like (HRL) traits are characteristic of spontaneous lesions including yellowish spots, brown spots or white-stripe that appeared randomly and dispersedly on all the leaves in the absence of plant pathogens. Our previous studies have shown that the wheat line P7001 showed an HRL trait at low nitrogen supply, and this trait was controlled by the gene Ndhrl1 (Nitrogen-dependent hypersensitive reaction-like 1). In order to investigate the robustness of the trait expression mediated by Ndhrl1 under different genetic backgrounds, seven genetic populations, with P7001 being the common female parent, were constructed and analyzed. F1 plants from six of the seven combinations showed HRL trait and Ndhrl1 segregated in a dominant way of HRL: non-HRL = 3:1 in the six populations (F2). Exceptionally, the F1 plants of P7001/Fielder combination showed non-HRL trait and HRL trait in the F2 population showed a contrasting recessive segregation ratio of HRL: non-HRL = 1:3, suggesting Fielder may have another HRL-related gene. Using 55K SNP array and PCR-based markers, the HRL-related gene in Fielder was mapped to an interval of 5.63-12.91Mb on the short arm of chromosome 2B with the flanking markers Yzu660R075552 and Yzu660F075941. A recombinant with genomic region of Fielder atNdhrl1 locus showingHRL trait demonstrated that Fielder also harbored Ndhrl1 same as P7001. Thus, Fielder carries a single dominant suppressor of Ndhrl1, designated as Ihrl1 (Inhibitor of hypersensitive reaction-like). Interestingly, Ihrl1 is tightly linked to Ndhrl1 and may be also involved in nitrogen metabolic and (or) signaling pathways.

Capturing Wheat Phenotypes at the Genome Level.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

BackgroundThe superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system.ResultWe selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient (R2) is greater than 0.7156. The root mean square error (RSME) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber.ConclusionIt is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.

A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images

Detecting and characterizing spikes from wheat field images is essential in wheat growth monitoring for precision farming. Along with various technological developments, deep-learning-based methods have remarkably improved wheat spike detection performance. However, detecting small and overlapping wheat spikes in UAV images is still challenging because high spike occlusion and complex background can cause error detection and miss detection problems. This paper proposes a deep learning method for oriented and small wheat spike detection (OSWSDet). Unlike classical wheat spike detection methods, OSWSDet introduces the orientation of wheat spikes into the YOLO framework by integrating a circle smooth label (CSL) and a micro-scale detection layer. These improvements enhance the ability to detect small-sized wheat spikes and prevent wheat spike detection errors. The experiment results show that OSWSDet outperforms classical wheat spike detection methods, and the average accuracy (AP) is 90.5%. OSWSDet can accurately detect spikes in UAV images with complex field backgrounds and provides technical references for future field wheat phenotype monitoring.