Browsing by Subject "Training Population Optimization"

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    Combating Fusarium Head Blight Resistance in Wheat with Genomic Selection and Computer Vision Technology
    (2022-01) Adeyemo, Emmanuel
    Fusarium head blight (FHB), primarily caused by Fusarium graminarum, Schwabe, is a devastating fungal disease that limits wheat production globally and can significantly reduce yield and grain quality. At the University of Minnesota, screening for FHB begins at the F5 stage and continues annually until the line is released as a cultivar. Before implementing genomic selection at the F5 stage in 2016, we evaluated ~ 3,000 F5 lines annually in field nurseries. The use of genomic selection allowed the prediction of untested lines with a training population of 500 lines selected by pedigree information. The first study showed that a set of 200 lines selected by genomic relationship led to predictive abilities of up to 0.49, whereas a larger, randomly selected subset of 500 F5 lines had a maximum predictive ability of 0.34. While the addition of parents also led to increased predictive abilities, the increments were not significant in most cases. The second study examined the merit of incorporating available germplasm into our existing genomic selection pipeline. We observed that training populations that contained historical data were less useful while those that included a subset of 200 F5 lines selected by genomic relationship, were more effective for predicting FHB. The third study demonstrated the use of computer vision to estimate the percentage of kernels damaged by Fusarium. We utilized 85 samples containing five check cultivars with varying levels of FHB susceptibility and maturity and achieved an accuracy of 90%. Additional studies should be done to assess the utilization of this technology among our experimental lines.