Main subject area: 3D-imaging; Canopy model; DeepLearning; Plant phenotyping
Grain yield of perennial grain crops (e.g. Intermediate wheatgrass) is low – a bottle neck for domestication. Due to their complex genomic structure, phenomic selection that relies on intensive phenotyping on plant traits can potentially be used for development of these novel crops. This requires visualization of grain shape, spike geometry and canopy architecture high-throughput which can speed up the identification of ideal plant candidates with greater grain yield potential. This master project aims to pipeline the process of plant canopy visualization in 3D by acquiring images and constructing models from thousands of individual plants.
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 or 60 ECTS: Experimental theses in which the student is responsible for collection and analysis of his/her own original data
Additional information
The master student will mainly work on some of the followings: data acquisition (canopy imaging), data processing (AI-training), and data analysis in both and field conditions, if chosen the 45 ECTS option. If 30 ECTS, the main activity will be on image analysis and statistics to be incorporated into the thesis.
Relevant articles
Salter WT, Shrestha A, Barbour MM (2021) Open source 3D phenotyping of chickpea plant architecture across plant development. Plant Methods 17:95. https://doi.org/10.1186/s13007-021-00795-6
