Image-based plant analysis for sustainable agriculture

Image-based plant phenotyping allows the quantification of traits (e.g. leaf area, number, colour) reflecting plant performance non-destructively. This approach helps in deciphering the complex genetic-environment interactions influencing its performance and finding the best-adapted crop varieties to local environmental constraints for sustainable agriculture. Deep learning methods are necessary to speed up analyses from large image datasets. For this PhD project, we are looking for a motivated student with expertise in deep learning to develop classification, segmentation, and regression models for plant image analysis. The successful candidate should be experienced with a major deep learning library for python.

Candidate Profile

Interested applicants should:

  • Have a first class Honours degree or Masters degree at Distinction level, in a discipline relevant to the area of study.
  • Prove highly motivated to tackle challenging research problems
  • The potential to engage in innovative research and to complete the PhD within a prescribed period of study.

Applicants whose first language is not English must meet the University’s English language requirements.


Applicants should email the following documents to Ms Laura Cooper; email:

  • A two-page research proposal for the selected research topic that demonstrates an understanding of the background to the area and outlines the questions that the applicant is interested in researching.
  • Curriculum vitae
  • Cover letter (one page) explaining why the candidate is interested in applying for this studentship.
  • Names and contact details of two referees

All applications must be received by 13th July 2022. Those who have not been contacted by 7th August 2022 should assume that they have been unsuccessful. The studentship is expected to start in October 2022 or March 2023.

Do not hesitate to contact me for any informal enquires.

Categories: Opportunity