## 5 Conclusions  
The development of modern cameras, drones, and other image acquisition equipment, as well as artificial intelligence remote sensing integration technology, has greatly contributed to the advancement of precision agriculture applications. In this paper, a solution is proposed for the use scenario of rapid 3D reconstruction of jujube trees in Xinjiang jujube garden, and the specific conclusions are as follows:  
Proposed a feature extraction network model based on the composite U-Net network structure for feature extraction. Aiming at the specific image sampling process in the jujube garden, jujube trees are briefly obscured, scale change occurs, and the target field of view is briefly lost. The accurate target tracking of jujube trees is realized by using the translation filter and scale filter that introduces the multi-feature fusion mechanism. Combined with the key frame automatic extraction algorithm, the high-efficiency multi-view angle and equal-scale image acquisition of jujube tree trunks are realized. Enhancement of feature extraction network using DRE-Net structure improved by self-attention mechanism. Aiming at the multi-view image of the jujube tree, the growth of branches is complicated, too much interference information, and the situation of feature extraction is not obvious. In the feature extraction network model based on a convolutional neural network, the most critical deep feature information in the image is effectively extracted by continuously performing convolution operations on the image. The U-Net-based composite improvement network model can retain richer detailed features while obtainingfeature maps with different scales, improving the characterization ability of features, expanding the range of sensory fields of features, and retaining the global information of the original image and perfecting the details through feature splicing and fusion, so that the subsequent depth estimation results are more accurate and complete. This paper proposes a self-attention mechanism to improve the feature enhancement network of DRE-Net. Aiming at the decrease of completeness in the process of jujube trunk feature extraction. Using the characteristics of the self-attention mechanism to pay more attention to the key information in the input data and the effect of DRE-Net to avoid overfitting through dense residual connections, the feature extraction network is enhanced to extract more semantic information of the image and realize the performance improvement of the double indexes of accuracy and completeness. In the link of cost volume regularization, a multi-scale cost volume information fusion network based on a hierarchical feature body decoder is designed to increase the interconnection between different layers of cost volumes so that the information within the cost volumes can be transferred layer by layer to improve the estimation quality of the depth map. The algorithm proposed in this paper is compared with other traditional reconstruction and deep learning-based 3D reconstruction methods on the DTU dataset, and compared with the traditional algorithm, it performs better in terms of completeness, accuracy, and robustness; and compared with similar methods based on deep learning, the model in this paper performs more balanced. After the ablation test of the actual dataset of jujube trees, all modules of the improved model in this paper effectively improve the accuracy and completeness of the multi-view stereo matching 3D reconstruction, which achieves good results in practice and puts forward a feasible program reference for the integration of remote sensing and artificial intelligence into the existing agricultural system. However, there is still room for further improvement and enhancement of the work in this paper. Due to the increase of convolutional scale, this leads to the increase of GPU's occupied memory and the increase of training cost.