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FoodBioSystems DTP - Reducing potato losses by creating a predictive model for black dot disease

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  • Full or part time
    Prof L A Terry
    Prof J Doonan
    Ms T Jeary
    Dr F Rezwan
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description


United Nations Sustainable Development Goal 12.3 aims to reduce food losses and waste by 50% by 2030. Black dot disease (Colletotrichum coccodes) is a significant cause of pre-pack quality loss for the UK potato industry costing £3M per annum. Black dot accounts for up to a 50 % loss during packhouse grading and the disease is predicted to become more problematic with climate change as UK pre-pack production moves from South to North.

Little research has been carried out on understanding the impact of environmental factors associated with the black dot disease incidence and the host/pathosystem and the underlying mechanisms, which govern susceptibility from field to store, still remain elusive. The soil-borne pathogen infects tubers in the field but only tends to manifest itself during postharvest storage and these outbreaks are notoriously difficult to predict.

This PhD project will test whether in field environmental monitoring, digital plant phenotyping and improved postharvest management can be combined to predict black dot disease to avoid losing pre-pack quality during cold storage. The work aims to use sophisticated photonics and associated algorithms, machine learning and data integration methods across the pre and postharvest continuum to create a predictive model for black dot incidence and severity during storage and evaluate how resilience can be improved in response to different climate change scenarios. The model would predict when best to market a crop in cold storage and thereby reduce food loss and waste.

The main research objectives of this project include: (1) Evaluate the effect of variety and preharvest ‘field factors’ (e.g. elevated temperature, soil type, inoculum load, horticultural maturity, fungicide application) on disease incidence using controlled growth studies and industry-led field trials, (2) Validate the use of photonic plant phenotyping and machine learning to associate canopy traits and tuber quality during storage, with black dot incidence, (3) Evaluate the impact of postharvest curing (temperature pull down after harvest) and use of dynamically controlled storage to monitor tuber status and manage disease severity, (4) provide guidelines to storage practitioners on how to link field factors with better management of black dot to reduce loss and better inform storage release.

Training opportunities:

This is a cross-disciplinary project involving elements of plant pathology, digital phenotyping, machine learning, crop and disease modelling, and postharvest biology and pathology. The student will have opportunity to learn practical skills in potato agronomy, harvest, storage, packhouse operations and technical quality management at Albert Bartlett in Scotland and Eastern England. The student would attend in-house training in using plant phenotyping at Cranfield and Aberystwyth. In addition, the student would enroll on the two-week MSc modules in ‘Machine learning for Metabolomics’ and ‘Postharvest Technology’ as part of the MSc in Applied Bioinformatics and MSc in Food Systems Management, respectively, at Cranfield.

Student profile:

This project would be suitable for students with a degree in plant science.

Funding Notes

This project is part of the FoodBioSystems BBSRC Doctoral Training Partnership (DTP), it will be funded subject to a competition to identify the strongest applicants. Due to restrictions on the funding, this studentship is only open to UK students and EU students who have lived in the UK for the past three years.

The PhD studentship is half funded by Albert Bartlett.

The FoodBioSystems DTP is a collaboration between the University of Reading, Cranfield University, Queen’s University Belfast, Aberystwyth University, Surrey University and Brunel University London. Our vision is to develop the next generation of highly skilled UK Agri-Food bioscientists with expertise spanning the entire food value chain. We have over 60 Associate and Affiliate partners. To find out more about us and the training programme we offer all our postgraduate researchers please visit

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