GrainQuest - using Artifical Intelligence and high resolution multimodal imaging to dissect the developmental and genetic basis of seed composition
Lead Research Organisation:
Aberystwyth University
Department Name: IBERS
Abstract
Protein content is important for barley and wheat. High oil oat lines may lead to novel food products such as non-dairy yogurts. However, grains arise from a double fertilization event that produces a composite structure with three genetically distinct tissues or compartments - the maternal pericarp, the embryo and the endosperm. Their relative sizes and compositions contribute different constituents to overall grain composition.
Hypothesis: The relative sizes and compositions of different grain compartments vary between genotypes, affecting overall composition.
Objectives:
1) Machine learning tools: To develop robust deep learning protocols, a set of characterized test grains will be used to train neural networks to recognize and quantify features that may be related to compositional variation.
2) Co-registration across imaging modalities: To validate the outputs from AI, composition must be directly measured in the different compartments. Therefore, intact grains will be CT scanned and imaged using a hyperspectral camera and analysed using LA-REIMS, an emerging technique for spatial analysis of endogenous complex lipids and metabolites at a high level of chemical identification. LA-REIMS results will be spatially co-registered with the other imaging modalities, directly testing the predicted composition
3) Verification of concept: To formally address whether variation in these novel traits is associated with genetic variation (and therefore be useful to breeders), we will use grain from genetically unstructured (mapping) populations that have been previously created by crossing selected pairs of oats with distinctive grain composition. We will build quantitative models to account for the genotypic contribution to variation in features contributing to yield and compositional quality and compare to models produced using conventional whole grain data. This will provide a rigorous test of the hypothesis in a meaningful genetic context.
Hypothesis: The relative sizes and compositions of different grain compartments vary between genotypes, affecting overall composition.
Objectives:
1) Machine learning tools: To develop robust deep learning protocols, a set of characterized test grains will be used to train neural networks to recognize and quantify features that may be related to compositional variation.
2) Co-registration across imaging modalities: To validate the outputs from AI, composition must be directly measured in the different compartments. Therefore, intact grains will be CT scanned and imaged using a hyperspectral camera and analysed using LA-REIMS, an emerging technique for spatial analysis of endogenous complex lipids and metabolites at a high level of chemical identification. LA-REIMS results will be spatially co-registered with the other imaging modalities, directly testing the predicted composition
3) Verification of concept: To formally address whether variation in these novel traits is associated with genetic variation (and therefore be useful to breeders), we will use grain from genetically unstructured (mapping) populations that have been previously created by crossing selected pairs of oats with distinctive grain composition. We will build quantitative models to account for the genotypic contribution to variation in features contributing to yield and compositional quality and compare to models produced using conventional whole grain data. This will provide a rigorous test of the hypothesis in a meaningful genetic context.
Organisations
People |
ORCID iD |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/T008776/1 | 30/09/2020 | 29/09/2028 | |||
| 2879608 | Studentship | BB/T008776/1 | 30/09/2023 | 29/09/2031 |