RESUMO
Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.
RESUMO
Stem rust caused by Puccinia graminis f. sp. tritici Eriks. is a dangerous disease of common wheat worldwide. Development and cultivation of the varieties with genetic resistance is one of the most effective and environmentally important ways for protection of wheat against fungal pathogens. Field phytopathological screening and genome-wide association study (GWAS) were used for assessment of the genetic diversity of a collection of spring wheat genotypes on stem rust resistance loci. The collection consisting of Russian varieties of spring wheat and introgression lines with alien genetic materials was evaluated over three seasons (2016, 2017 and 2018) for resistance to the native population of stem rust specific to the West Siberian region of Russia. The results indicate that most varieties displayed from moderate to high levels of susceptibility to P. graminis; 16% of genotypes had resistance or immune response. In total, 13,006 single-nucleotide polymorphism (SNP) markers obtained from the Infinium 15K array were used to perform genome-wide association analysis. GWAS detected 35 significant marker-trait associations (MTAs) with SNPs located on chromosomes 1A, 2A, 2B, 3B, 5A, 5B, 6A, 7A and 7B. The most significant associations were found on chromosomes 7A and 6A where known resistance genes Sr25 and Sr6Ai = 2 originated from Thinopyrum ssp. are located. Common wheat lines containing introgressed fragments from Triticum timopheevii and Triticum kiharae were found to carry Sr36 gene on 2B chromosome. It has been suggested that the quantitative trait loci (QTL) mapped to the chromosome 5BL may be new loci inherited from the T. timopheevii. It can be inferred that a number of Russian wheat varieties may contain the Sr17 gene, which does not currently provide effective protection against pathogen. This is the first report describing the results of analysis of the genetic factors conferring resistance of Russian spring wheat varieties to stem rust.