Purpose <p>3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select 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.</p> Methods <p>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.</p> Results <p>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, time series clustering produced more stable results than pointwise clustering with comparable accuracy. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development.</p> Conclusions <p>Spatio-temporal phenotyping did not outperform point-wise methods in peak accuracy but delivered markedly more stable genotype separation. Plant-level traits dominated discrimination, enabling simplified phenotyping. Early growth stages were most informative, with maximal separability at 72–75 days after sowing, contributing to breeding and variety approval.</p>