Award details

Unlocking the potential of wheat grain heterogeneity using machine vision

Reference	BB/W006979/1
Principal Investigator / Supervisor	Professor Ian Fisk
Co-Investigators / Co-Supervisors	Dr Rahul Bhosale, Dr Michael Foulkes
Institution	University of Nottingham
Department	Sch of Biosciences
Funding type	Research
Value (£)	766,942
Status	Current
Type	Research Grant
Start date	01/07/2022
End date	31/03/2026
Duration	45 months

Abstract

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

Summary

This project will unlock the potential of wheat grain heterogeneity. We will: 1) Develop a novel single seed phenotyping tool based on hyperspectral imaging technology (HSI) integrated with next generation machine learning 2) Explain the determinism of the variance of uniformity of single seed grain quality parameters and explore a broad range of both known, and novel and exotic wheat genotypes for previously undefinable unique single seed traits, this will allow breeders to target previously unavailable grain quality uniformity traits, as well as speed selection from segregating populations. 3) Deploy the single grain HSI technology as a novel molecular breeding tool by determining key genes controlling single grain quality uniformity traits and validating the candidate genes by developing lines with contrasting expressing of the novel genes which we will test in field experiments. 4) Demonstrate the application of the single seed phenotyping tool as a sorting technology at laboratory and pilot production scale for wheat. This will demonstrate the ultimate value of the approach by producing exemplar food products (bread, biscuit and malted wheat) with enhanced quality and health credentials and validating the findings through sensory and consumer insight testing. Ultimately this project offers the potential for breeders to significantly upgrade the UK wheat grain production, reduce the requirements to use imported wheat of millers, and enhance the nutritional quality and sensory quality traits of bread, biscuits and food products containing malted wheat for the consumer. The impact of this project will be very significant as sorting by hyperspectral classification for protein content would allow tighter segregation of the wheat supply chain into defined applications such as those that require lower protein (cakes, biscuits, pastry) from those that require higher protein with good protein quality and consistency and resulting good rheology (bread, pasta, high protein flour) and allow tighter adherence to supplier specifications in addition to reducing the need of imported wheat. At the highest capacities, a single sorting machine can process around 0.5 million tons per year, this indicates a very significant impact on the UK wheat industry with a relatively low-cost intervention, often in centralised milling sites. Furthermore, premium wheat with unique bread-making properties (e.g. elevated micronutrients, very high protein) and unique flavour potential through the malting process, will be sold with a price premium. If a further 20% of UK farmers growing bread-making wheat varieties were to achieve the grain protein market specification of 13% for the premium each year, it would be worth an extra £25 M per year to the UK agriculture sector.

Committee	Research Committee B (Plants, microbes, food & sustainability)
Research Topics	Crop Science, Plant Science, Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	LINK: Responsive Mode [2010-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

Associated awards:

BB/W005751/1 Unlocking the potential of wheat grain heterogeneity using machine vision

BB/W005972/1 Unlocking the potential of wheat grain heterogeneity using machine learning-enabled machine vision

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