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Fusarium head blight (FHB) of cereals is the major head disease negatively affecting grain production worldwide. In 2016 and 2017, serious outbreaks of FHB occurred in wheat crops in Poland. In this study, we characterized the diversity of Fusaria responsible for these epidemics using TaqMan assays. From a panel of 463 field isolates collected from wheat, four Fusarium species were identified. The predominant species were F. graminearum s.s. (81%) and, to a lesser extent, F. avenaceum (15%). The emergence of the 15ADON genotype was found ranging from 83% to 87% of the total trichothecene genotypes isolated in 2016 and 2017, respectively. Our results indicate two dramatic shifts within fungal field populations in Poland. The first shift is associated with the displacement of F. culmorum by F. graminearum s.s. The second shift resulted from a loss of nivalenol genotypes. We suggest that an emerging prevalence of F. graminearum s.s. may be linked to boosted maize production, which has increased substantially over the last decade in Poland. To detect variation within Tri core clusters, we compared sequence data from randomly selected field isolates with a panel of strains from geographically diverse origins. We found that the newly emerged 15ADON genotypes do not exhibit a specific pattern of polymorphism enabling their clear differentiation from the other European strains.

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Wheat infections caused by fungi of the genus Fusarium decrease yields and have serious economic consequences. The produced mycotoxins have harmful effects on human and animal health. The aim of this study was to develop classification models based on selected textural parameters to distinguish between infected and healthy wheat kernels. The classification accuracy of kernels positioned on the ventral side was determined at 78-100% in the model based on textural parameters from hyperspectral images, and at 95-100% based on images generated by a flatbed scanner. Kernels positioned on the dorsal side were correctly classified in 78-98% based on hyperspectral images, and in 92-100% based on colour images. In the models combining textural parameters from the ventral and dorsal sides of wheat kernels, classification accuracy reached 76-98% in hyperspectral images, and 94-100% in images generated by a flatbed scanner. The imaging technique-flatbed scanner and the ventral side of the kernels provided higher classification accuracy results. The results will contribute to further research aiming to develop models for the determination of fungal chemotypes and/or fungal species based on selected textural features of wheat kernels.

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