This project will compare the utility of cutting-edge multispectral Imaging sensors with low-tech "traditional" methods for the screening of markers of drought-stress in cultivars of potato that are commonly grown commercially in the UK.
Department: Crop Science
Supervised by: Dr Luke Bell
Potato is a relatively drought-sensitive crop but can produce more calories per unit of land and water than any other crop when well-watered. Breeding has done little to improve the drought tolerance of potato over the last ~100 years, so attention must be paid to the improvement of water and crop management strategies. Recent advances in phenotyping technologies, specifically imaging and image-processing, allow the rapid screening of a crop for drought, and other, stresses. Once a stress-marker has been identified, whole fields can be screened for that marker with unmanned arial and ground vehicles. These markers are poorly understood in potato, with only leaf greenness and canopy temperature having been studied to any useful extent. Even these traits have only been characterised in a limited number of cultivars, few of which are grown commercially in the UK. This project will build on previous work by this group by characterising these markers, amongst others, in a wider range of cultivars, including those most widely grown in the UK. Data already collected by this group suggests that previously published results on leaf greenness may not apply to all potato cultivars. The student will grow a range of cultivars in pots and use a 3D multispectral imaging system to look for differences between well-watered and water-restricted plants. This will be compared with more "traditional" measures, including handheld SPAD meter measurements as a proxy for leaf greenness.
Preparation and maintenance of a glasshouse pot-experiment. Supervised operation of a 3D multispectral imaging system. Measurement of potato canopy traits with handheld phenotyping devices (inc. SPAD meter). Post-harvest tuber analyses, including dry matter measurements, to validate the methods used.
The student should be reliable, have good communication skills, and be well organised. Knowledge and experience of plant cultivation and working in glasshouse environments is desirable.
Critical and sceptical reading of previously published research. Use of a 3D multispectral imaging system and handheld phenotyping devices. Management and analysis of large datasets.
Crop & Environment Laboratory (CEL), School of Agriculture
10 am - 4 pm, Mon - Fri
Monday 30 May 2022 - Friday 08 July 2022
The post will be advertised centrally on the UROP website between 21st February and 4th April 2022. Students should submit their CV and Cover Letter directly to the Project Supervisor (click on supervisor name at the top of the page for email). Successful candidates will be invited for an interview.