FOODBIOSYSTEMS - Reducing potato losses by creating a predictive model for black dot disease PhD
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 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 therebyreduce food loss and waste.
Main research objectives:
(1) Evaluate the effect of variety and preharvest (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.
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.
Funding for PhD studentships from BBSRC is only available to successful candidates who meet the eligibility criteria set out in the UK Research and Innovation (UKRI) harmonized training terms and conditions which you can find here.
In most cases, UK and EU nationals who have been ordinarily resident in the UK for 3 years prior to the start of the course are eligible for a full-award. Other EU nationals may qualify for a fees-only award. All candidates should check the UKRI harmonised postgraduate training terms and conditions to confirm their eligibility for funding. International students who do not meet these nationality requirements should contact specific Schools/Departments to discuss funding opportunities.
European Union (non-UK) candidates who have not lived in the UK for at least the past three years may apply for a BBSRC Fee-Only Award. You will have to provide your own living costs. To apply for a fee-only award, write a separate request (200 words maximum) explaining why you need this funding.