Unlocking the potential of wheat grain heterogeneity using machine vision

This project unlocks the potential of wheat grain heterogeneity using machine vision. We will develop a novel single seed hyperspectral imaging technology (HSI) integrated with next generation machine learning and use this to develop phenotyping tools to improve uniformity of grain quality traits in UK wheat and implement a seed sorting solution for the UK food industry (bread, biscuit and malted wheat). We have demonstrated the potential of HSI for the non-destructive prediction of total protein content in single wheat grains and quantified striking genetic variation of wheat grain protein uniformity in the Watkins wheat landrace panel. We will carry out GWAS analysis and identify novel marker-trait associations.The associated genes/loci identified in the GWAS will be further prioritized through transcriptomics analysis on contrasting lines to identify genes differentially expressed for grain uniformity loci. We will also carry out detailed physiological field studies on subsets of contrasting lines to quantify effects of phenology, ear architecture and canopy traits to help interpret the basis of the variance in homogeneity of the grain quality traits We will validate the allelic variation of prioritized candidate genes through developing and evaluating NILs and RILs for target genetic loci. In addition our plant breeding partner (DSV) will phenotype breeding parents using HSI imaging to assess grain protein content uniformity and crosses will be selected and progressed to F5 yield and grain quality plots as a demonstration of wheat breeding for enhanced grain quality through single seed HSI screening. Finally together with our partners in the food industry (CBRI, LECO, NFI and ABM) we will demonstrate the benefits of highly controlled protein levels and ratios of protein subclasses by developing food products (bread, biscuit and malted wheat) at commercial scale and evaluating these products by flavour, sensory and consumer profiling