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Jujube is widely cultivated in China because of their extremely high nutritional value and medical value. With the increase of the planting area, the shortcomings of manual management have become increasingly prominent, and it is urgent to realize the information management of jujube orchards. With the rise of smart agriculture, computer technology and agricultural production are combined to build digital agriculture. To realize the intelligent pruning of jujube trees, a three-dimensional reconstruction of jujube trees in Xinjiang was carried out. This study proposed a three-dimensional reconstruction method of fruit trees under a natural lighting environment based on point cloud registration. Moreover, aiming at the strict requirements of the spatial position of the registered point cloud, an improved point cloud registration algorithm was proposed based on the traditional Iterative Closest Point (ICP) algorithm for the strict requirements of the spatial position of the registered point cloud. Firstly, color images and depth images of fruit trees from different perspectives were collected by using an RGB-D camera, and point cloud acquisition under corresponding perspectives was achieved through information fusion. Secondly, Data preprocessing of fruit trees’ each piece of point cloud was carried out for background removing and original point cloud de-noising based on depth distance judgment and spare noise filtering methods respectively. The region of interest was extracted by setting the segmentation threshold based on the histogram of the number of point clouds, and accordingly, each relative accurate data set was obtained as the jujube tree’s point cloud in each specific perspective. Then, there were three target balls which were artificial markers near the root, and the artificial marking method was used to realize the initial cloud registration of the two sites. Finally, on the basis of initial registration, the surface normal vector and curvature of the point cloud were calculated, and the points with similar curvature formed a pair of points. The kd-tree was used to establish a high-dimensional index tree data structure to structure to decrease the cost of running time of point cloud registration. Then, the ICP algorithm was used to complete precise registration. The registered point cloud was triangulated using the Alpha-shape algorithm to achieve surface reconstruction. The above-mentioned methods of initial registration and precise registration were used to globally register multiple jujube trees and completely reconstruct the three-dimensional model of fruit trees. The experimental results showed that by introducing the initial registration, the accuracy and stability of the point cloud registration were effectively improved. The registration error was controlled within 1.0 cm, and the average registration error was 0.76 cm. The reconstructed model had a strong sense of reality and was closer to the real-world tree in appearance. The relative error between the ground truth of the branch and the reconstructed value was controlled within 7%, and the accuracy of the reconstructed model was higher. The reconstruction model had high accuracy, which could provide a visual research foundation and technical support for intelligent jujube pruning. [ABSTRACT FROM AUTHOR]

This note, using a three-dimensional model of river flow and sediment transport, examines the effect of the vertical resolution and the choice a nonequilibrium adaptation length Ls in predicting flow and sediment transport around groins in China’s Yongding River. The results show that a fine vertical grid and nonequilibrium sediment transport model provide good predictions, especially on the river bed profile with an obvious main channel and flood plain. [ABSTRACT FROM AUTHOR]