Slow stripe rusting in Chinese wheat Jimai 44 conferred by Yr29 in combination with a major QTL on chromosome arm 6AL.

KEY MESSAGE: YrJ44, a more effective slow rusting gene than Yr29, was localized to a 3.5-cM interval between AQP markers AX-109373479 and AX-109563479 on chromosome 6AL. "Slow rusting" (SR) is a type of adult plant resistance (APR) that can provide non-specific durable resistance to stripe rust in wheat. Chinese elite wheat cultivar Jimai 44 (JM44) has maintained SR to stripe rust in China since its release despite exposure to a changing and variable pathogen population. An F2:6 population comprising 295 recombinant inbred lines (RILs) derived from a cross between JM44 and susceptible cultivar Jimai 229 (JM229) was used in genetic analysis of the SR. The RILs and parental lines were evaluated for stripe rust response in five field environments and genotyped using the Affymetrix Wheat55K SNP array and 13 allele-specific quantitative PCR-based (AQP) markers. Two stable QTL on chromosome arms 1BL and 6AL were identified by inclusive composite interval mapping. The 1BL QTL was probably the pleiotropic gene Lr46/Yr29/Sr58. QYr.nwafu-6AL (hereafter named YrJ44), mapped in a 3.5-cM interval between AQP markers AX-109373479 and AX-109563479, was more effective than Yr29 in reducing disease severity and relative area under the disease progress curve (rAUDPC). RILs harboring both YrJ44 and Yr29 displayed levels of SR equal to the resistant parent JM44. The AQP markers linked with YrJ44 were polymorphic and significantly correlated with stripe rust resistance in a panel of 1,019 wheat cultivars and breeding lines. These results suggested that adequate SR resistance can be obtained by combining YrJ44 and Yr29 and the AQP markers can be used in breeding for durable stripe rust resistance.

Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices.

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.