A method and a system for extracting leaf growth parameters of fruit trees based on clustering segmentation

Abstract

The invention provides a method and a system for extracting leaf growth parameters of fruit trees based on clustering segmentation, the method comprising the following steps: superclustering the pointcloud data of the canopy branches and leaves of a target fruit tree, and performing LCCP clustering on a plurality of adjacent voxel blocks in the obtained voxel block set to obtain a first clustering set; applying Kmeans clustering to any point group in the first clustering set to obtain the second clustering set; according to the point cloud data corresponding to each point group in the secondclustering set, obtaining the growth parameters of each leaf based on boundary extraction. After LCCP clustering is used to segment the point group obtained by superbody clustering, and the Kmeans clustering algorithm based on dynamic K-value is further adopted. The improved clustering Kmeans algorithm can automatically obtain the K value, the shortcoming of manual setting the K value in the traditional algorithm is overcome, the point cloud data segmentation of the canopy branches and leaves of the target fruit trees becomes more complete and more thorough, and then extracted leaf growth parameters are more accurately.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of three-dimensional reconstruction, and more specifically, to a method and system for extracting growth parameters of fruit tree leaves based on clustering and segmentation. Background technique [0002] The canopy of fruit trees is the main place for photosynthesis of fruit trees. The morphological structure and spatial distribution of its branches and leaves directly affect the quality and yield of fruits. Clustering and segmenting the branches and leaves of fruit trees and further extracting the growth parameters of leaves can provide a comprehensive overview of the morphological structure of the canopy of fruit trees. Analysis and calculation of light distribution and fruit tree shaping and pruning provide a theoretical basis. Scholars at home and abroad have carried out a lot of work on tree point cloud data processing and growth parameter extraction. With the increase in the prod...

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Problems solved by technology

[0004] At present, there are relatively few studies on the clustering and segmentation of fruit tree leaves with large trees. Most of the clustering and segmentation methods are aimed at the segmentation of independent objects in scene files, or the segmentation of planes and cylinders with regular characteristics. The point cloud segmentation clustering method for fruit tree leaves is also more inclined to fruit trees with larger leaves, fewer numbers, and opposite phyllotaxy. The data complexity is low, and the requirements for the robustness and applicability of the algorithm are relatively low.

For example, for common apple trees, since the leaves of apple trees are smaller and denser and the growth phyllotaxy is spiral, the extraction of growth parameters has higher requirements on the integrity and detailed description of the leaf point cloud. The traditional Kmeans algorithm, DBSCAN density aggregation The results obtained by the class algorithm and the Region-growing algorithm cannot obtain a complete single leaf, and are not suitable for the clustering and segmentation of branch and leaf point clouds with small and compact leaves such as apples and apple trees and whose growth phyllotaxy is spiral, which leads to The extracted leaf growth parameters are inaccurate

[0005] For the extraction of leaf growth parameters, most of the existing technologies use the projection method to reduce the dimension of the 3D point cloud and convert it into a 2D image to solve the longest and shortest distances as the leaf length and leaf width, but ignore the leaf in space. Conditions such as curling occur, resulting in a decrease in the resulting growth parameter compared to the true value

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