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Intelligent, Energy-efficient De-leafing for the Soft Fruit Industry 

Prof Elizabeth Sklar, University of Lincoln; Prof Greg Keeffe, Queen’s University Belfast; Sarah Palmer, Driscolls Genetics Limited (Industry Supervisor)

Research Aims

The overarching aim of this project is to develop intelligent methodologies that can inform and improve the 'de-leafing' process for the soft fruit industry, as well as measure the energy usage associated with the process. In nature, plants that produce fruit also produce leaves; but when such plants are cultivated for fruit production, it is desirable to prune excess leaves and concentrate the plant's energy on growing the fruit to be harvested. This also makes the fruit more accessible and faster to pick.


This project will explore the application of intelligent robots to undertake de-leafing, including investigation of in-field practices conducted by human workers and study of plant science strategies to optimise fruit yield.

The need for automated de-leafing is rapidly increasing as the availability of human workers to perform this task decreases. This means that the job does not get done and farms are less efficient. One of the key technologies underlying intelligent approaches is computer vision and the application of relevant machine learning techniques to help identify and count fruits and leaves. However, machine learning can require many computational cycles to provide accurate estimates, which in turn can require substantial amounts of energy.

The work conducted on this project will help contribute intelligent, energy-aware solutions that help improve farm productivity and efficiency, with an eye towards sustainable approaches that measure and reduce the amount of energy used by the solutions considered.

The successful candidate will have the opportunity to visit commercial farms in the UK and in California, US. They will work closely with plant breeding experts attached to the sponsoring company. The successful candidate will develop their technical skills, computer vision, machine learning and human-robot coordination.

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Interview date

Apply for this studentship

See our Application Page.

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