Extraction method of fruit tree branch skeleton

Abstract

The invention provides an extraction method for a fruit tree limb skeleton. The extraction method for the fruit tree limb skeleton comprises the following steps: a three dimensional point cloud data set of a fruit tree and a color value of each data of the cloud data set are obtained; the three dimensional point cloud data set is segmented according to the color value of the data point to obtain a main branch data set and a sprout data set; a main branch skeleton is extracted from the main branch data set; a sprout skeleton is extracted from the sprout data set; the main branch skeleton and the sprout skeleton are subjected to a skeleton composition process to obtain the limb skeleton of the fruit tree. The extraction method for the fruit tree limb skeleton of the invention raise the speed and the quality of the fruit tree limb skeleton extraction.

Description

technical field [0001] The invention relates to the technical field of computer graphics, in particular to a method for extracting fruit tree branch skeletons. Background technique [0002] The 3D reconstruction and quantitative analysis of the morphological structure of fruit trees is a hot issue in the fields of agronomic research, graphic simulation and popular science education. Compared with other plants such as field crops and flowers, fruit trees not only have taller shapes, but also have more complex canopy structures. This brings great challenges to the information collection and automatic reconstruction of fruit tree morphological structure in the leafy state. [0003] Branches are an important part of the morphological structure of fruit trees and play a decisive role in shaping the shape of fruit trees. Therefore, the accurate extraction of fruit tree branch skeleton is one of the most important contents to realize the 3D reconstruction of fruit tree morphologic...

AI Technical Summary

Problems solved by technology

The above-mentioned existing technologies all use large-scale 3D laser scanners to obtain point cloud data of trees, and then perform tree branch skeleton extraction and 3D reconstruction. Twigs are often difficult to extract directly from scan data, so these methods generate twigs by applying some random rules to achieve visually "real" reconstructions, but such reconstructions do not achieve a true sense of "based on measurement data." The reconstructed three-dimensional tree canopy structure is still quite different from the real tree, especially the length, density, and spatial orientation of the twigs may have a large error from the actual one, which is difficult to apply to the tree canopy. Research and analysis on layer light distribution characteristics, spatial distribution of branches and leaves, plant type characteristics, etc.

However, this method also has the following problems: First, the method does not segment the 3D point cloud when extracting the trunk skeleton of the fruit tree, so it needs to be extracted from the 3D point cloud data of the whole fruit tree, and the running speed of the algorithm is relatively slow. At the same time, due to the influence of canopy leaf information, the accuracy of the extracted branch skeleton will also be affected; the second is that this method only extracts the skeleton of the main branch when extracting the skeleton of the fruit tree branch, while the young branch is passed through later. The spatial distribution model of the canopy of the twigs of this type of fruit tree increases, thus reducing the accuracy of the final reconstructed 3D model of the fruit tree

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