Applying advanced molecular biology, metabolomics and image analysis using machine-learning technology to improve wheat resistance against Fusarium head blight

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Wheat is a staple crop and an integral part of the Canadian economy, providing income for producers and jobs for Canadians. Canada is the sixth largest producer of wheat in the world and the second largest exporter (~20% of the world market). The Canadian wheat industry, including both bread and durum wheat, is frequently threatened by fungal diseases, such as Fusarium head blight (FHB), which causes hundreds of millions of dollars in losses every year in Canada. Typical FHB symptoms include premature bleaching of spikelets, discolouration of the rachis, and Fusarium damaged kernels (FDK). The light weight of FDKs and even a small amount of FDKs within a commercial wheat crop can result in severe yield and quality losses. FHB is also a food and feed safety concern due to the contamination of grain by the mycotoxin deoxynivalenol (DON). Farmers will likely experience grade loss, restricted marketing opportunities, added costs, and lost income as a result of FHB. Compared to chemical fungicides, cultivation of resistant wheat varieties is the most efficient, eco-friendly and often the most economic strategy to control FHB. With advanced molecular biology, genomics, metabolomics and diverse automated imaging tools, the proposed project aims to provide a comprehensive understanding of the molecular mechanism of FHB resistance in wheat, characterize the genetic basis of the types of FHB resistance, develop useful molecular markers, identify resistant germplasm for breeding programs, and develop the high-throughput imaging and deep-learning tools required to support these advancements. Eventually, the knowledge, germplasm, molecular markers and phenotyping tools generated from this project will accelerate breeding cycles and benefit the wheat industry.