3D Reconstruction and Volume Estimation of Jujube Using Consumer-Grade RGB-Depth Sensor

Li, Jian; Wu, Ming-qing; Li, Hengzheng

doi:10.1109/access.2023.3285713

Cited by 2 publications

(1 citation statement)

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“…It is worth mentioning that TabNet (Tabular Neural Networks), as a method for dealing with large amounts of structured data involved, is commonly used in various scenarios in the agricultural field, especially in agricultural data analysis, crop prediction, and optimization [6][7][8]. In this paper, we mainly utilize remote sensing, artificial intelligence, and other technologies to extract image information from the trunk of jujube trees and carry out three-dimensional reconstruction work on them so as to provide the necessary working basis for the subsequent management work of jujube tree gardens 2 of 18 (e.g., tree pruning [9,10], volume estimation of jujube [11], pest and disease monitoring [12,13], orchard management [14], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Multi-View Jujube Tree Trunks Stereo Reconstruction Based on UAV Remote Sensing Imaging Acquisition System

Ling,

Li,

Ding

et al. 2024

Applied Sciences

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High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization 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.

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