Automatic reconstruction and modeling of dormant jujube trees using three-view image constraints for intelligent pruning applications

Abstract

Automatic reconstruction and modeling of dormant jujube trees provide a basis and prerequisite for intelligent pruning applications in large-scale, dwarf, and high-density planting orchards. For accurate 3D reconstruction of the trunk and pruning branches, this study presents a method based on a multi-view image sequence of dormant jujube trees. It involves four main steps. First, we determine the way of image acquisition, equipment parameters and posture, and reconstruction accuracy by investigating and analyzing their planting mode to meet the requirements of robot intelligent pruning operation. Second, performing image matching in images of dormant jujube trees is challenging due to few local feature regions, weak continuity, and difference in individual morphological structures. We provide a bilateral image-matching method based on the three-view geometry constraint. Third, this study optimizes a camera self-calibration algorithm based on image EXIF tags by broadening its applicability to improve reconstruction efficiency. It does not rely on the particular geometric constraints in the specific scenes. Finally, a dense 3D point cloud of a tree is built automatically with this improved SfM reconstruction strategy and combined with the PMVS algorithm. Both qualitative and quantitative evaluations are performed on generating 3D models of eight dormant jujube trees and their number of branches, average plant height, and crown are used as indicators. The results show that our method identifies the number of pruning branches with an accuracy of 92% and provides a performance of 95% for measuring the diameter of the plant height and canopy. The results provide visual research basis and technical support for the intelligent pruning of jujube trees.