Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm

Abstract

The dormant pruning of jujube is a labor-intensive and time-consuming activity in the production and management of jujube orchards, which mainly depends on manual operation. Automatic pruning using robots could be a better way to solve the shortage of skilled labor and improve efficiency. In order to realize automatic pruning of jujube trees, a method of pruning point identification based on skeleton information is presented. This study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees. The space colonization algorithm acts on the global point cloud to generate the skeleton of jujube trees. The iterative relationship between skeleton points was represented by constructing a directed graph. The proposed skeleton analysis algorithm marked the skeleton as the trunk, the primary branches, and the lateral branches and identified the pruning points under the guidance of pruning rules. Finally, the visual model of the pruned jujube tree was established through the skeleton information. The results showed that the registration errors of individual jujube trees were less than 0.91 cm, and the average registration error was 0.66 cm, which provided a favorable database for skeleton extraction. The skeleton structure extracted by the space colonization algorithm had a high degree of coincidence with jujube trees, and the identified pruning points were all located on the primary branches of jujube trees. The study provides a method to identify the pruning points of jujube trees and successfully verifies the validity of the pruning points, which can provide a reference for the location of the pruning points and visual research basis for automatic pruning.

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

“…This improvement method, which utilized dual perspectives, effectively resolved issues such as unstable growth direction due to a small field of view, premature termination of growth, and the premature impact of tree branching on skeleton point growth caused by an excessively large field of view. Simultaneously, adjustments and optimizations were made to the skeleton containing noisy branches, and a solution was proposed for the issue of nearly parallel branches on the skeleton, enhancing the correctness and rationality of the skeleton's topological representation [23,[32][33][34]. Furthermore, a completion strategy for the missing parts of the skeleton was designed.…”

“…In general, the current computerized 3D reconstruction based on tree TLS point clouds mainly includes theories and methods such as clustering or segmentation, graph theory, iterative modeling, and spatial colonization [32]. The first three methods have now been proven to be used in TLS point clouds to measure tree biomass and carbon stock, while the spatial colonization algorithm originated from an algorithm proposed by Runions to solve leaf vein generation and was extended to tree modeling in subsequent studies, with the core idea of competing for the surrounding point clouds during the growth process and setting the deletion thresholds, influence radius, and step sizes to control the growth magnitude and direction, among other things [33]. The algorithm can utilize the principle of tree competition for space to reflect the branch growth trend and generate a continuous skeleton.…”

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

“…The skeleton extraction algorithm is a technique used to extract the central line or skeleton of an object in a binary image ( Fu et al., 2023 ). By progressively shrinking connected regions within the object contour, the algorithm produces a concise contour that provides valuable information for image processing tasks like recognition and matching.…”

Vid2Cuts: A Framework for Enabling AI-Guided Grapevine Pruning

“…These studies either focus on the entire pipeline for fully automated pruning [14], [15], [20], [22], [26] or separate steps that are essential for an autonomous pruning system. For instance, machine vision systems have been developed for precise branch detection [16]- [18], [24], [25], reconstruction [21], [23], [27], [28], skeletonization [29]- [31] and localization of cut positions [32]- [34].…”