Student information
MSc thesis topic: From pot-to-plot: Correlating high-throughput phenotyping data from a greenhouse experiment to field performance
Image-based phenotyping platforms allow us to monitor plants behavior in a very detailed manner. These details are very useful, especially if we could use this information to predict the crops performance in the field. In this thesis, we would like to study this potential by comparing high-throughput data from a greenhouse experiment to field performance.
In a previous experiment, we have exposed different quinoa varieties to drought stress. We used the high-throughput phenotyping system of NPEC - which allows us to track the plants development, physiology and photosynthesis response to drought over time. Simultaneously, we have used the same advanced lines in field experiments (with control and drought stress). We obtained performance data of the advanced lines in the field by drone imaging. We offer an interesting internship position where you'll be able to work on the data that is obtained in both greenhouse and field experiments.
Relevance to research/projects at GRS or other groups
- The research is conducted within the Plant breeding group, in collaboration with prof.dr.ir. L (Lammert) Kooistra from the GRS group.
Objectives and Research questions
- How can we predict biomass, height, and panicle size using the available 3D and 2D data from plants in pots?
- How accurately can biomass, height, and panicle size be predicted from drone data collected on advanced plant lines?
- What is the correlation between performance in pots and performance in plots, specifically addressing whether pot data can predict overall plot performance?
Requirements
- Experience in R and Python
- Basic knowledge on image analysis
Literature and information
- High-Resolution Analysis of Growth and Transpiration of Quinoa Under Saline Conditions (Jaramillo Roman et al, 2021)
Theme(s): Sensing & measuring, Modelling & visualisation