Abstract:

[Objective] Plant leaf shape is an important part of plant architectural model. Establishment of a three-dimensional structural model of leaves may assist simulating and analyzing plant growth. However, existing leaf modeling approaches lack interpretability, invertibility, and operability, which limit the estimation of model parameters, the simulation of leaf shape, the analysis and interpretation of leaf physiology and growth state, and model reusage. Aiming at the interoperability between three-dimensional structure representation and mathematical model parameters, this study paid attention to three aspects in wheat leaf shape parametric reconstruction: (1) parameter-driven model structure, (2) model parameter inversion, and (3) parameter dynamic mapping during growth. Based on this, a set of parameter-driven and point cloud inversion model for wheat leaf interoperability was proposed in this study. [Methods] A parametric surface model of a wheat leaf with seven characteristic parameters by using parametric modeling technology was built, and the forward parametric construction of the wheat leaf structure was realized. Three parameters, maximum leaf width, leaf length, and leaf shape factor, were used to describe the basic shape of the blade on the leaf plane. On this basis, two parameters, namely the angle between stems and leaves and the curvature degree, were introduced to describe the bending characteristics of the main vein of the blade in the three-dimensional space. Two parameters, namely the twist angle around the axis and the twist deviation angle around the axis, were introduced to represent the twisted structure of the leaf blade along the vein. The reverse parameter estimation module was built according to the surface model. The point cloud was divided by the uniform segmentation method along the Y-axis, and the veins were fit by a least squares regression method. Then, the point cloud was re-segmented according to the fit vein curve. Subsequently, the rotation angle was precisely determined through the segment-wise transform estimation method, with all parameters being optimally fit using the RANSAC regression algorithm. To validate the reliability of the proposed methodology, a set of sample parameters was randomly generated, from which corresponding sample point clouds were synthesized. These sample point clouds were then subjected to estimation using the described method. Then error analyzing was carried out on the estimation results. Three-dimensional imaging technology was used to collect the point clouds of Zhengmai 136, Yangmai 34, and Yanmai 1 samples. After noise reduction and coordinate registration, the model parameters were inverted and estimated, and the reconstructed point clouds were produced using the parametric model. The reconstruction error was validated by calculating the dissimilarity, represented by the Chamfer Distance, between the reconstructed point cloud and the measured point cloud. [Results and Discussions] The model can effectively reconstruct wheat leaves, and the average deviation of point cloud based parametric reconstruction results is about 1.2 mm, which has a high precision. Parametric modeling technology based on prior knowledge and point cloud fitting technology based on posterior data was integrated in this study to construct a digital twin model of specific species at the 3D structural level. Although some of the detailed characteristics of the leaves were moderately simplified, the geometric shape of the leaves could be highly restored with only a few parameters. This method was not only simple, direct and efficient, but also had more explicit geometric meaning of the obtained parameters, and was both editable and interpretable. In addition, the practice of using only tools such as rulers to measure individual characteristic parameters of plant organs in traditional research was abandoned in this study. High-precision point cloud acquisition technology was adopted to obtain three-dimensional data of wheat leaves, and pre-processing work such as point cloud registration, segmentation, and annotation was completed, laying a data foundation for subsequent research. [Conclusions] There is interoperability between the reconstructed model and the point cloud, and the parameters of the model can be flexibly adjusted to generate leaf clusters with similar shapes. The inversion parameters have high interpretability and can be used for consistent and continuous estimation of point cloud time series. This research is of great value to the simulation analysis and digital twinning of wheat leaves.

Key words: leaf, wheat, parametrization, point cloud, inversion