Projects

We are currently advertising National Productivity Investment Fund Industrial CASE studentships with a deadline of the 16th July 2018.

27 / 06 / 2018

Deep Learning to Accelerate Breeding for Crop Disease Resistance (BROWN_J18ICASENF)

how to apply

Deep learning is a novel, exciting development in artificial intelligence, particularly suited to solving complex problems involving image analysis. It broadly mimics the way a human brain writes its own rules to learn how to categorise different objects in the environment. This project will give a student an outstanding opportunity to apply this fast-developing technology to a current issue of great practical importance.

A key problem in agriculture is how to control crop diseases without pesticides. Plant breeders wish to release new varieties which are resistant to all significant diseases but at present, this requires a highly-trained person to assess several diseases and stresses in a field trial simultaneously. This is slow and difficult, not least because many pathogens produce spots and blotches of a superficially similar appearance.

The aim of this project is to develop a deep learning network which is capable of being trained and retrained to identify, distinguish and quantify different diseases. This will be applied in field situations to increase the accuracy and rigour of disease screening in breeding trials to select new plant varieties.

We welcome applications from students in computational science who wish to learn how to develop applications of artificial intelligence to challenging real-world problems, or from biologists with exceptionally good skills in computing.

The supervisors are James Brown, a plant pathologist who works on durable disease resistance, and Richard Morris, a physicist with a strong interest in advanced computation. JIC has outstanding facilities for high performance computing, image analysis and plant pathology. This is an iCASE project in collaboration with three leading plant breeders, KWS, Limagrain and RAGT Seeds.