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EASTBIO Plant and product phenotyping in wheat using hyper-spectral imaging and machine learning

PHD Opportunities

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Dr S Hoad , Prof Mike Coffey , Dr A Hamilton No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

The aim of this EASTBIO studentship is to advance phenotyping in wheat plants and products using hyper-spectral sensing data coupled with machine learning. SRUC is at the leading edge in applying spectral sensing in agriculture e.g. prediction of livestock traits and milk quality. The combined technologies of multi- and hyper-spectral imaging and machine learning have already been used by SRUC to create phenotype signatures to inform animal health and predict performance.

This studentship will gather high quality and high-volume phenotype signatures from: (1) wheat plants and crop in order to generate predictive equations for genotype performance, including yield and quality and/or (2) wheat products e.g. grains and straw in order to generate predictive equations for sample quality and traits of value in downstream processing in food and feed sectors. As well as substantial research outputs, an outcome will be phenotypes and tools for wheat breeders and the crop improvement research community.

Key objectives are to:
(1) Quantify variation in: (1) plant and crop growth and development and/or (2) product quality and processing trait signals from multi- and hyper-spectral imaging.
(2) Optimise spatial resolution in spectral imaging at the trait, plant and crop level for genotype and product discrimination.
(3) Use crop and sample data to predict: (1) crop performance and/or sample quality and processing value.
(4) Publish wheat phenotype plant and crop product signatures that link phenotype to genotype for downstream genetic dissection.

The project will involve a programme of field, glasshouse and laboratory work over 3 years (e.g. 3 growing periods) to link imaging from multi- and hyper-spectral cameras, from outdoor and indoor rigs, including a UAV (drone) for field studies, with whole plant and plant product e.g. grain and straw measurements. In years 1 and 2, the student will gather phenotype data in densely-sampled (i) field-grown crop plots and (ii) lab grain and straw samples in a diverse collection wheat genotypes from a multi-parent advanced generation inter-cross (MAGIC) population, sourced from NIAB, and previously genotyped. A third experimental year will be used for validation and gap-filling in plant and/or product (sample) information. In order to widen phenotypic expression we will use: (i) agronomic treatments, such as fertilizer and crop protection inputs, will be used manipulate plant and crop structure e.g. leaf canopy in the field and/or glasshouse and (ii) sample manipulation protocols, such and grain and straw handling, will be used to manipulate grain/sample quality. Initial work will include testing of sample size and spatial resolution according to the range of phenotypic expression.
After data collection, interrogation of multi-spectral imaging with machine learning will test how different spectral signatures relate to phenotypic expression, based on physical and chemical measures that relate to plant performance and product quality, or processing value, as well genotype. Data processing embracing state of the art deep learning techniques will be used to examine spectral signatures – from traits and whole plants – for development of predictive tools for crop performance and sample quality and processing potential. Statistical inference, whereby properties of the underlying ‘population’ of data are estimated, will explore specificity and sensitivity of the spectral signatures according to the phenotype expressed and genotype. Crop models will be trained on SRUC’s machine learning server harnessing the computational power supplied by 4 Nvidia Tesla v100 GPUs. Variation in phenotypic expression will enable the project to establish a set of reference phenotypes that can be used to link performance and quality/processing traits to genotype.

Compared with existing methods and protocols for crop monitoring and evaluation our integrated spectral imaging and machine learning approach has potential for producing high quality and high-volume phenotype signatures. This project, and the student employed, would support research and industry capability for crop characterisation and linking phenotype with genotype, with application to crop improvement and breeding.
Applicants should download the required forms from and send the following documents to [Email Address Removed]:
a. EASTBIO Application Form
b. EASTBIO DTP Equality Form
c. CV
d. Academic transcripts (a minimum of an upper second class or first class honours degree or equivalent is required for PhD study
e. Two references should be provided by the deadline using the EASTBIO reference form ( Please advise your referees to return the reference form to [Email Address Removed].
f. If you are nominated by the supervisor(s) of the EASTBIO PhD project you wish to apply for, they will provide a Supervisor Support Statement.

Funding Notes

This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership This opportunity is open to UK and International students and provides funding to cover stipend and UK level tuition (Please state if your institution will provide funding to cover the difference in fees). Please refer to UKRI website and Annex B of the UKRI Training Grant Terms and Conditions for full eligibility criteria.


Two references should be provided by the deadline using the EASTBIO reference form ( Please advise your referees to return the reference form to

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