Abstract
Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.
Highlights
Plant leaves show different 3D architecture during growth [1], and their spatial appearance shows sensitive responses to environmental conditions, such as drought stress [2,3,4], cold stress [5], and light availability [6]
The aim of this study is to provide a parametric surface modeling method for accurately fitting tea leaf point cloud into non-uniform rational B-spline (NURBS) surface
A parametric surface modelling method was proposed for tea leaf point cloud
Summary
Plant leaves show different 3D architecture during growth [1], and their spatial appearance shows sensitive responses to environmental conditions, such as drought stress [2,3,4], cold stress [5], and light availability [6]. A previous study [13] has found a quadratic function relationship between lettuce fresh weight and leaf surface areas estimated from convex and concave hulls, which could be used for estimation of crop yield with further investigation. Some morphological characteristics, such as leaf length, leaf width, leaf area, specific leaf area, leaf inclination angle, and leaf bend angle, have been directly taken as research objects among these studies. Almost no consideration was given to three dimensional morphological traits, which can be used to accurately describe leaf shape and can provide more nuanced understandings to leaf’s dynamic adaptions to environmental conditions
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