Remote Sensing (Sep 2022)
Virtual Laser Scanning Approach to Assessing Impact of Geometric Inaccuracy on 3D Plant Traits
Abstract
In recent years, 3D imaging became an increasingly popular screening modality for high-throughput plant phenotyping. The 3D scans provide a rich source of information about architectural plant organization which cannot always be derived from multi-view projection 2D images. On the other hand, 3D scanning is associated with a principle inaccuracy by assessment of geometrically complex plant structures, for example, due the loss of geometrical information on reflective, shadowed, inclined and/or curved leaf surfaces. Here, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures, including tomato, maize, cucumber, and arabidopsis. For this purpose, virtual laser scanning of synthetic models of these four plant species was used. This approach was applied to simulate different scenarios of 3D model perturbation, as well as the principle loss of geometrical information in shadowed plant regions. Our experimental results show that different plant traits exhibit different and, in general, plant type specific dependency on the level of geometrical perturbations. However, some phenotypic traits are tendentially more or less correlated with the degree of geometrical inaccuracies in assessing 3D plant architecture. In particular, integrative traits, such as plant area, volume, and physiologically important light absorption show stronger correlation with the effectively visible plant area than linear shoot traits, such as total plant height and width crossover different scenarios of geometrical perturbation. Our study addresses an important question of reliability and accuracy of 3D plant measurements and provides solution suggestions for consistent quantitative analysis and interpretation of imperfect data by combining measurement results with computational simulation of synthetic plant models.
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