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PhD Studentship: Image analysis for automated risk assessment in modern agriculture

  • Full or part time
  • Application Deadline
    Sunday, March 01, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Application details:
Reference number: CO/HF-DTP/2020
Start date of studentship: 1 July 2020
Closing date of advert: 1 March 2020
Interview date: 17 March 2020

Primary supervisor: Dr Hui Fang
Secondary supervisor: To be confirmed

Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.

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Full Project Detail:
In this century, robotics and sensing technologies have been widely applied in agricultural sector to increase the crop production more efficiently. For example, agricultural drone has been used to monitor the plant growth and make early detection of plant disease via hyperspectral image analysis. In addition, high semantic image understanding is also a fundamental step for precision agriculture, e.g. the control of pesticide spraying and/or crop fertilization.

In this project, you will develop novel deep learning methods to improve the analysis accuracy of robotics and sensing data (most of them will be imagery data) under the concept of precision agriculture. The developed algorithms are expected to be used in low-cost consumer applications. At the deployment stage, these methods can be easily implemented in affordable embedded systems.

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Entry requirements:

Essential qualifications and skills:
• An upper-second class honours bachelor's degree in Computer Science.
• Experience or a working knowledge in machine learning, data mining, or relevant AI areas.
• Experience or a working knowledge in algorithm development and software engineering.
• Programming skill: Python, C++, or Matlab.
• Ability to write project reports and make technical presentations to academic research groups.

Preferable attributes and experience:
• Experience in carrying on theoretic study using mathematically sound approaches.
• Working knowledge in deep learning and its frameworks.
• Excellent written and oral communication skills.
• Self-motivated with ability to meet deadlines and achieve technical objectives at a high standard.
• Strong real-world problem-solving skills.

Contact details:
Name: Dr Hui Fang
Email address:
Telephone number: +44 (0)1509 222579

How to apply:
All applications should be made online at Under programme name, select Computer Science.

Please quote reference number: CO/HF-DTP/2020.

Funding Notes

The 3-year studentship will provide a tax-free stipend of £15,009 per year, plus tuition fees at the UK/EU rate (currently £4,327 per year). Whilst we welcome applications from non-EU nationals, please be advised that it will only be possible to fund the tuition fees at the international rate and no stipend will be available.

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