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Yellow rust is a worldwide disease that poses a serious threat to the safety of wheat production. Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images.

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions.

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.

Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale.

Wheat stripe rust is a fungal disease caused by Puccinia striiformis f. sp. tritici . The outbreak of wheat stripe rust will have a great impact on wheat production in Xinjiang, China. In order to identify resistance to wheat stripe rust and the distribution of resistance genes in 82 wheat cultivars (41 spring wheat and 41 winter wheat), wheat seedling resistance was evaluated using CYR32, CYR33 and CYR34, and wheat adult plant stage resistance was identified using a combination of 3 races. Six molecular markers were used to identify Yr29 , Yr39 , Yr46 , Yr69 and YrTr1 in 82 wheat cultivars. The results showed that 3 of 82 wheat cultivars (Xinchun No.14, Xinchun No.22, and Xindong No.22) were immune to stripe rust at the adult plant stage. Xinchun No.29, Xinchun No.32, Xindong No.5 and Xindong No.29 were resistant at all stage. The highest detection rates were for Yr69 and YrTr1 , at 78.05% and 76.83%. However, the detection rates for Yr39 and Yr46 were only 0 and 2.44%, respectively. The Xindong No.22 were detected with the most resistance genes, which included 4 Yr genes. Furthermore, Xindong No.22 were immune to the disease at adult plant stage. The results confirmed the resistance gene distribution of the wheat cultivars in Xinjiang were heterogeneously, and the number of Yr genes was significantly and positively correlated with wheat cultivars resistant to stripe rust.

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions.

Diseases characterized by long distance inoculum dispersal (LDD) are among the fastest spreading epidemics in both natural and managed landscapes. Management of such epidemics is extremely challenging because of asymptomatic infection extending at large spatial scales and frequent escape from the newly established disease sources. We compared the efficacy of area- and timing-based disease management strategies in artificially initiated field epidemics of wheat stripe rust and complemented with simulations from an updated version of the spatially explicit model EPIMUL, using model parameters relevant to field epidemics. The model was further used to expand the number of epidemic mitigations beyond that feasible to incorporate in the field. The field experiment was conducted for 2 years in two locations having different climatic conditions. Culling and protection treatments were applied at different times after epidemic initiation and to different spatial extents surrounding the outbreaks. In each experiment, treatments were replicated four times in plots 33.5 m long and 1.52 m wide with a 0.76 × 0.76 m inoculated focus centered within each plot. Disease gradients were assessed along the center lines of the plots at 1.52 m intervals both upwind and downwind from the focus. Both field and simulation results indicated that control measures applied over the entire population were highly effective in suppressing the epidemics by more than 99% but may not always be logistically and economically feasible at large spatial scales. Comparison between the variable sized treatment areas and application timings suggested that implementing contiguous premises (CP) cull at 1 day after first sporulation in the outbreak focus reduced rust by 52% and 60% in Corvallis and Madras, respectively. However, altering the cull size did not significantly affect the disease epidemic development, which suggested that early timing had a greater influence in suppressing the epidemics than did increased area of application. However, sufficiently large, treated areas may compensate for a delay in application timing to some extent. Results from these replicated treatments may help to devise appropriate management strategies for other LDD pathogens.

Summary Alternative splicing (AS) is a crucial post‐transcriptional regulatory mechanism in plant resistance. However, whether and how plant pathogens target splicing in their host remains mostly unknown. For example, although infection by Puccinia striiformis f. sp. tritici (Pst), a pathogenic fungus that severely affects the yield of wheat worldwide, has been shown to significantly influence the levels of alternatively spliced transcripts in the host, the mechanisms that govern this process, and its functional consequence have not been examined. Here, we identified Pst_A23 as a new Pst arginine‐rich effector that localizes to host nuclear speckles, nuclear regions enriched in splicing factors. We demonstrated that transient expression of Pst_A23 suppresses plant basal defence dependent on the Pst_A23 nuclear speckle localization and that this protein plays an important role in virulence, stable silencing of which improves wheat stripe rust resistance. Remarkably, RNA‐Seq data revealed that AS patterns of 588 wheat genes are altered in Pst_A23‐overexpressing lines compared to control plants. To further examine the direct relationship between Pst_A23 and AS, we confirmed direct binding between two RNA motifs predicted from these altered splicing sites and Pst_A23 in vitro. The two RNA motifs we chose occur in the cis‐element of TaXa21‐H and TaWRKY53, and we validated that Pst_A23 overexpression results in decreased functional transcripts of TaXa21‐H and TaWRKY53 while silencing of TaXa21‐H and TaWRKY53 impairs wheat resistance to Pst. Overall, this represents formal evidence that plant pathogens produce ‘splicing’ effectors, which regulate host pre‐mRNA splicing by direct engagement of the splicing sites, thereby interfering with host immunity.

Summary Wheat stripe rust caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is one of the most destructive wheat diseases resulting in significant losses to wheat production worldwide. The development of disease‐resistant varieties is the most economical and effective measure to control diseases. Altering the susceptibility genes that promote pathogen compatibility via CRISPR/Cas9‐mediated gene editing technology has become a new strategy for developing disease‐resistant wheat varieties. Calcineurin B‐like protein (CBL)‐interacting protein kinases (CIPKs) has been demonstrated to be involved in defence responses during plant‐pathogen interactions. However, whether wheat CIPK functions as susceptibility factor is still unclear. Here, we isolated a CIPK homoeologue gene TaCIPK14 from wheat. Knockdown of TaCIPK14 significantly increased wheat resistance to Pst, whereas overexpression of TaCIPK14 resulted in enhanced wheat susceptibility to Pst by decreasing different aspects of the defence response, including accumulation of ROS and expression of pathogenesis‐relative genes. We generated wheat Tacipk14 mutant plants by simultaneous modification of the three homoeologues of wheat TaCIPK14 via CRISPR/Cas9 technology. The Tacipk14 mutant lines expressed race‐nonspecific (RNS) broad‐spectrum resistance (BSR) to Pst. Moreover, no significant difference was found in agronomic yield traits between Tacipk14 mutant plants and Fielder control plants under greenhouse and field conditions. These results demonstrate that TaCIPK14 acts as an important susceptibility factor in wheat response to Pst, and knockout of TaCIPK14 represents a powerful strategy for generating new disease‐resistant wheat varieties with BSR to Pst.

Background Stripe rust, caused by Puccinia striiformis f. sp. tritici ( Pst ), poses a threat to global wheat production. Deployment of widely effective resistance genes underpins management of this ongoing threat. This study focused on the mapping of stripe rust resistance gene YR63  from a Portuguese hexaploid wheat landrace AUS27955 of the Watkins Collection. Results YR63  exhibits resistance to a broad spectrum of Pst races from Australia, Africa, Asia, Europe, Middle East and South America. It was mapped to the short arm of chromosome 7B, between two single nucleotide polymorphic (SNP) markers sunCS _ YR63  and sunCS_67 , positioned at 0.8 and 3.7 Mb, respectively, in the Chinese Spring genome assembly v2.1. We characterised YR63  locus using an integrated approach engaging targeted genotyping-by-sequencing (tGBS), mutagenesis, resistance gene enrichment and sequencing (MutRenSeq), RNA sequencing (RNASeq) and comparative genomic analysis with tetraploid (Zavitan and Svevo) and hexaploid (Chinese Spring) wheat genome references and 10+ hexaploid wheat genomes. YR63  is positioned at a hot spot enriched with multiple nucleotide-binding and leucine rich repeat (NLR) and kinase domain encoding genes, known widely for defence against pests and diseases in plants and animals. Detection of YR63  within these gene clusters is not possible through short-read sequencing due to high homology between members. However, using the sequence of a NLR member we were successful in detecting a closely linked SNP marker for YR63  and validated on a panel of Australian bread wheat, durum and triticale cultivars. Conclusions This study highlights YR63  as a valuable source for resistance against Pst in Australia and elsewhere. The closely linked SNP marker will facilitate rapid introgression of YR63  into elite cultivars through marker-assisted selection. The bottleneck of this study reinforces the necessity for a long-read sequencing such as PacBio or Oxford Nanopore based techniques for accurate detection of the underlying resistance gene when it is part of a large gene cluster.