Spatio-Temporal 4D Phenotyping for Automated Morphological Genotype Differentiation of Sugar Beet

3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. This study aims to disclose the benefit of incorporating dynamic spatio-temporal development of 3D parameters for automated crop genotype differentiation. A greenhouse experiment was conducted covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed over time, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and the noticeable higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial genotypic variations in the dynamic development of 3D morphological parameters could be demonstrated. The higher and more stable clustering performance using time series analysis underlines the importance of 4D data for plant genotype differentiation. Future work should focus on identifying important growth stages for data collection.