Main subject area: Image analysis; spectroscopy; chemometric
Expansion of cropland with more diversified plant communities can potentially contribute to more sustainable production of food, feed, and energy in the future. However, this requires a better understanding of the compatibility of plant species used in crop mixtures, and pairing the right belowground traits is the key to productive and sustainable crop diversification. Current tools do not allow species differentiation without destructive sampling. This makes the study on crop mixtures and their belowground interactions laborious causing the reduction in the research scale. This master project aims to validate the use of a novel root phenotyping tool for species differentiation under crop mixtures by exploiting novel spectroscopy and high throughput multi-trait detection from roots.
Any time
Blichers Alle 20, Tjele, 8830-DK
30 ECTS: Theoretical thesis based on literature studies and/or analysis of issued and edited data sets.
45 ECTS: Experimental theses in which the student is responsible for collection and analysis of his/her own original data
The master student will mainly learn and work on some of the followings: data acquisition (root imaging; chemical, DNA sample analysis), data processing (AI-training), and data analysis (multivariate statistical analysis)
Svane SF, Dam EB, Carstensen JM, Thorup-Kristensen K. A multispectral camera system for automated minirhizotron image analysis. Plant and Soil. 2019;441:657–672.
Bodner G, Nakhforoosh A, Arnold T, Leitner D. Hyperspectral imaging: a novel approach for plant root phenotyping. Plant Methods. 2018;14:84.
