Stripe rust is a foliar disease in wheat caused by Puccinia striiformis f. tritici. The best way to protect wheat from this disease is by growing resistant cultivars. Tetraploid wheat can serve as a good source of valuable genetic diversity for various traits. Here, we report the mapping of nine stripe rust resistance quantitative trait loci (QTL) effective against P. striiformis f. tritici in China and Israel. We used recombinant inbred lines (RILs) developed from a cross between the durum wheat cultivar Svevo and Triticum dicoccoides accession Zavitan. By genotyping the RIL population of 137 lines using the wheat 90K single-nucleotide polymorphism array, we mapped an adult-plant resistance locus QYrsv.swust-1BL.1, the most effective QTL, within a 0.75-centimorgan region in T. turgidum subsp. durum ‘Svevo’ on chromosome arm 1BL, corresponding to the region of 670.7 to 671.5 Mb on the Chinese Spring chromosome arm 1BL. Of the other eight minor-effect stripe rust QTL, seven were from Svevo and mapped on chromosomes 1A, 1B, 2B, 3A, 4A, and 5A, and one was from Zavitan and mapped on chromosome 2A. Several QTL with epistatic effects were identified as well. The markers linked to the resistance QTL can be useful in marker-assisted selection for incorporation of these resistance QTL into both durum and common wheat cultivars.

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset.

Changing climate and changes in cropping systems have greatly affected outbreaks of plant diseases. Wheat stripe rust is a disease posing a threat to global wheat production, caused by Puccinia striiformis f. sp. tritici (Pst). Pst oversummering regions play a crucial role in the emergence of new races in China. To unveil the migration pattern of oversummering to adjacent regions, we develop a set of KASP-SNP marker from 28 Pst whole-genome sequences to investigate the population structure in the oversummering and its adjacent regions. A set of 19 Chinese wheat differentials is used to characterize the virulence patterns of 308 sampled Pst isolates. By integrating virulence characterization, population genetic analysis, air trajectory simulation and field disease monitoring, two main Pst dispersal routes are identified. Inocula from Eastern Qinghai are dispersed to Western and Eastern Liupan Mountain, and reach Guanzhong Plain. The second route originates from Middle Gansu, then through Longnan, and reaches the Guanzhong Plain via Eastern Liupan Mountain. Both dispersal routes result in Pst inoculum spreading to the Huang-Huai-Hai region, the main wheat-growing region in China. The proposed migration routes can be used to develop disease management strategies at a regional and national scale.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases in wheat. Due to the large-scale and widely-distributed planting pattern of wheat, the directional selection pressure of the pathogen is very strong. Therefore, it is urgent to pyramid more stripe rust resistance genes in wheat cultivars to enhance resistance durability and ensure wheat production safety. In this study, two quantitative trait loci (QTL) for adult plant resistance (APR) to stripe rust, QYr.nafu-2BL and QYr.nafu-3BS, were validated and introgressed from wheat line P9897 into three Chinese elite wheat cultivars, Chuanmai 42, Xiangmai 25, and Zhengmai 9023, through marker validation. The three Chinese elite varieties were used as the female parent to cross with wheat line P9897, and they were selfed to the F6 generation. A total of 114 lines were then selected based on field agronomic traits and stripe rust resistance. Four markers (Xcfd73, Xgwm120, Xbarc87 and Xbarc133) linked with the QTL’s regions were employed to screen the 114 F6 lines. Subsequently, 27 lines combining two target QTL from P9897 were selected. The combination of agronomic traits and disease resistance results showed that 13 of these selected lines had favorable application prospects. The promising lines selected in this study could enrich the genetic resources of wheat stripe rust resistance genes, as well as provide material support and a theoretical basis for the prevention and control of wheat stripe rust in China.

Barley leaf stripe, caused by Pyrenophora graminea, is an essential systemic seed-borne disease in barley worldwide. Barley is a major cereal crop in the Qinghai–Tibet Plateau, and barley production has been threatened by leaf stripe in this region, particularly in organic farming regions. Detecting the pathogen in infected barley seeds is crucial for managing barley leaf stripe. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed to detect the pathogen based on primers designed based on the sequence of the pig 14 gene (GenBank: AJ277800) of P. graminea. The optimal concentrations of MgSO₄, dNTPs, and enzymes in the LAMP reaction system were established as 10.0 mM, 1.0 mM, and 8 U in a 25 μL reaction volume, respectively. The established LAMP methods for detecting P. graminea were optimally performed at 63 °C for 70 min with high reliability. The minimum detection limit was 1 × 10⁻² ng·μL⁻¹ in the 25 μL reaction system. The specificity of LAMP for P. graminea was validated with eight fungal species. All DNA extracts from P. graminea-infected barley seeds with incubation, intact, and smashed treatments were applied in LAMP and confirmed to enable the detection of the pathogen. The LAMP assay in this study could facilitate the detection of P. graminea in barley seeds onsite, provide information for seed health certificates, and help decide on seed treatment in leaf stripe management.

Hulless barley sheath rot is a spike disease caused by Dactylobotrys graminicola. In recent years, it has generally occurred in hulless barley growing areas in China, resulting in reduced hulless barley yields. In this study, primers and probes were designed based on conserved genome sequences, and a method was established using recombinant enzyme polymerase amplification-lateral flow burette (RPA-LFD) technology for the rapid diagnosis of sheath rot in hulless barley. The method can be successfully established in five minutes at a constant temperature of 39℃, and the results are consistent with those of normal PCR analysis. The method demonstrated high sensitivity, with a detection limit of 10 fg/µL. Furthermore, the rapid method was able to successfully detect D. graminicola in hulless barley during field incubation, which highlighted the significant advantage of the method in practical applications. In conclusion, the RPA method established in this study exhibited several advantageous characteristics, including high efficiency, simplicity, rapidity and practicality, which provide a theoretical basis for the early detection and prevention of D. graminicola.