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Computer Science: Fully Funded EPSRC DTP PhD Scholarship: AI-Based Crop Detection using Spectral Remote Sensing Images

   School of Mathematics and Computer Science

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  Dr Sara Sharifzadeh  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Funding providers: Engineering and Physical Sciences Research Council (EPSRC) DTP and Swansea University's Faculty of Science and Engineering

Subject areas: Artificial Intelligence, Image Analysis, Computer Vision, Machine Learning, Data Science

Project start date: 

  • 1 October 2022 (Enrolment open from mid-September)

Project supervisors: Dr Sara Sharifzadeh and Professor Xianghua Xie

Aligned programme of study: PhD in Computer Science

Mode of study: Full-time

Project description: 

The availability of free satellite images in medium/high spatial resolution has enabled possible solutions for the challenging dynamic land use and land cover mapping problems. This project is about developing new AI techniques for crop detection and mapping.

Crop detection is the first step in AI-based time series analyses, aiming to provide fundamental information for many socio-economic applications. Examples are crop control and yield estimation, change monitoring, supply chain and food security, climate change policies such as crop rotation, insurance, and fertilization services.

The lack of ground truth data is a major problem for crop detection. That is the case for most time-series analyses of historical data. On the other hand, the crop-specific variations in visual and chemical characteristics during a year are sensed by spectral satellite images. Therefore, this project is focused on developing AI techniques effectively utilizing the spectral bands for crop detection. This is achieved based on (1) an unsupervised framework to identify effective spectral bands for time-series analysis and crop detection. For this aim, the crops fingerprints and other vegetation indexes are used to identify the important spectral wavelengths. (2) a supervised framework for developing a novel spectral attention model using visual transformers prediction strategies.


Candidates must normally hold an undergraduate degree at 2.1 level plus a recognized master's degree with Merit (or Non-UK equivalent as defined by Swansea University) in Data Science, Computer Science, Mathematics, Industrial Engineering or closely related discipline.

Applications are sought from individuals with experience in basic knowledge of AI, machine learning and image analysis.

English Language requirements: If applicable – IELTS 6.5 overall (with at least 5.5 in each individual component) or Swansea recognized equivalent.

This scholarship is open to candidates of any nationality.

Funding Notes

This scholarship covers the full cost of tuition fees and an annual stipend of £16,062.
Additional research expenses will also be available.
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