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

Abstract

Abstract 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. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us 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 by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly 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 variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.

Plant phenotyping relevance

4Dの3Dモデルから植物形態形質を時系列抽出し、遺伝型識別のためのクラスタリング手法を評価することが研究の中心である。

Code and data availability

The paper's 4D sugar beet point cloud dataset (16 time points, 12 genotypes) is not yet public; the authors state it is available on reasonable request and will be published later. Extracted parameter values and Python analysis code are said to be in supplementary files, but no public URL for them is provided in thesup

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