###### Abstract  
High-quality agricultural multi-view stereo reconstruction technology is the key to precision and information in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme. 3D reconstruction; deep learning; multi-view stereo; remote sensing; feature extraction +
Footnote †: journal: Medical and Electrical Engineering, Shihezi University, Shihezi 832003, China; 2021209037@stsu. (e.g., tree pruning [9; 10], volume estimation of jujube [11], pest and disease monitoring [12; 13], orchard management [14], etc.). Using remote sensing and AI in sustainable agriculture poses challenges. For instance, video sequences from orchard cameras generate a substantial amount of redundant data. The quick and accurate extraction of required target image data from these sequences directly impacts the accuracy of subsequent AI model processing. Failures in this extraction process may lead to incorrect management decisions, potentially resulting in significant economic losses [15]. This paper begins with an algorithmic approach, using the key frame extraction algorithm based on target tracking to quickly extract high-quality remote sensing data from the orchard, and combined with the feature extraction enhancement network technology based on deep learning so as to efficiently and accurately generate the required three-dimensional model of the jujube tree, to help implement the refinement of the management and support for decision-making, and improve the efficiency of agricultural production [16]. This paper focuses on the MVS (Multi-view Stereo) technique, which can generate a 3D model of a scene target by using a set of target images from different viewpoints with known camera parameters [17]. Traditional MVS methods can usually be categorized into four types, including algorithms based on point clouds, voxels, variable polygon meshes, and depth maps. Although these methods achieve good results in ideal Lambertian scenes, they often fail to produce good reconstruction results in texture-poor, texture-repeated, or illumination-variable situations [18]. With the rapid development of artificial intelligence-related technologies such as deep learning, 3D reconstruction technology has ushered in great improvements. The application of convolutional neural networks to extract features and regularize cost volume makes MVS reconstruction more powerful. The first deep learning-based 3D reconstruction network in this field, MVSNet, was proposed by [PERSON] et al. [19] but suffers from the problem of poor reconstruction accuracy. Aiming at the problem that the MVSNet model using recurrent neural network does not effectively consider the contextual information, [PERSON] et al.