Stripe rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss., is a severe foliar disease of common wheat (Triticum aestivum L.) worldwide. Use of adult-plant resistance (APR) is an efficient approach to provide long-term protection of crops from the disease. The German spring wheat cultivar Naxos showed a high level of APR to stripe rust in the field. To identify the APR genes in this cultivar, a mapping population of 166 recombinant inbred lines (RILs) was developed from a cross between Naxos and Shanghai 3/Catbird (SHA3/CBRD), a moderately susceptible line developed by CIMMYT. The RILs were evaluated for maximum disease severity (MDS) in Sichuan and Gansu in the 2009-2010 and 2010-2011 cropping seasons. Composite interval mapping (CIM) identified four QTL, QYr.caas-1BL.1RS, QYr.caas-1DS, QYr.caas-5BL.3 and QYr.caas-7BL.1, conferring stable resistance to stripe rust across all environments, each explaining 1.9-27.6, 2.1-5.8, 2.5-7.8 and 3.7-9.1 % of the phenotypic variance, respectively. QYr.caas-1DS flanked by molecular markers XUgwm353-Xgdm33b was likely a new QTL for APR to stripe rust. Because the interval between flanking markers for each QTL was less than 6.5 cM, these QTL and their closely linked markers are potentially useful for improving resistance to stripe rust in wheat breeding.

Italian common wheat cultivars Libellula and Strampelli, grown for over three decades in Gansu province of China, have shown effective resistance to stripe rust. To elucidate the genetic basis of the resistance, F(3) populations were developed from crosses between the two cultivars and susceptible Chinese wheat cultivar Huixianhong. The F(3) lines were evaluated for disease severity in Beijing, Gansu and Sichuan from 2005 to 2008. Joint- and single-environment analyses by composite interval mapping identified five quantitative trait loci (QTLs) in Libellula for reduced stripe rust severity, designated QYr.caas-2DS, QYr.caas-4BL, QYr.caas-5BL.1, QYr.caas-5BL.2 and QYr.caas-7DS, and explained 8.1-12.4, 3.6-5.1, 3.4-8.6, 2.6 and 14.6-35.0%, respectively, of the phenotypic variance across four environments. Six interactions between different pairs of QTLs explained 3.2-7.1% of the phenotypic variance. The QTLs QYr.caas-4BL, QYr.caas-5BL.1 and QYr.caas-7DS were also detected in Strampelli, explaining 4.5, 2.9-5.5 and 17.1-39.1% of phenotypic variance, respectively, across five environments. Three interactions between different pairs of QTLs accounted for 6.1-35.0% of the phenotypic variance. The QTL QYr.caas-7DS flanked by markers csLV34 and Xgwm295 showed the largest effect for resistance to stripe rust. Sequence analyses confirmed that the lines with the QYr.caas-7DS allele for resistance carried the resistance allele of the Yr18/Lr34 gene. Our results indicated that the adult-plant resistance gene Yr18 and several minor genes confer effective durable resistance to stripe rust in Libellula and Strampelli.

Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most widespread and destructive wheat diseases worldwide. Growing resistant cultivars with adult-plant resistance (APR) is an effective approach for the control of the disease. In this study, 540 simple sequence repeat markers were screened to map quantitative trait loci (QTL) for APR to stripe rust in a doubled haploid (DH) population of 137 lines derived from the cross Pingyuan 50 x Mingxian 169. The DH lines were planted in randomized complete blocks with three replicates in Gansu and Sichuan provinces during the 2005-06, 2006-07, and 2007-08 cropping seasons, providing data for four environments. Artificial inoculations were carried out in Gansu and Sichuan with the prevalent Chinese race CYR32. Broad-sense heritability of resistance to stripe rust for maximum disease severity was 0.91, based on the mean value averaged across four environments. Inclusive composite interval mapping detected three QTL for APR to stripe rust on chromosomes 2BS, 5AL, and 6BS, designated QYr.caas-2BS, QYr.caas-5AL, and QYr.caas-6BS, respectively, separately explaining from 4.5 to 19.9% of the phenotypic variation. QYr.caas-5AL, different from QTL previously reported, was flanked by microsatellite markers Xwmc410 and Xbarc261, and accounted for 5.0 to 19.9% of phenotypic variance. Molecular markers closely linked to the QTL could be used in marker-assisted selection for APR to stripe rust in wheat breeding programs.

Identification of seedling and slow stripe rust resistance genes is important for gene pyramiding, gene deployment, and developing slow-rusting wheat cultivars to control the disease. A total of 98 Chinese lines were inoculated with 26 pathotypes of Puccinia striiformis f. sp. tritici for postulation of stripe rust resistance genes effective at the seedling stage. A total of 135 wheat lines were planted at two locations to characterize their slow rusting responses to stripe rust in the 2003-2004 and 2004-2005 cropping seasons. Genes Yr2, Yr3a, Yr4a, Yr6, Yr7, Yr9, Yr26, Yr27, and YrSD, either singly or in combinations, were postulated in 72 lines, whereas known resistance genes were not identified in the other 26 accessions. The resistance genes Yr9 and Yr26 were found in 42 and 19 accessions, respectively. Yr3a and Yr4a were detected in two lines, and four lines may contain Yr6. Three lines were postulated to possess YrSD, one carried Yr27, and one may possess Yr7. Thirty-three lines showed slow stripe rusting resistance at two locations in both seasons.

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.