Document Type
Article
Publication Date
10-2023
Keywords
3D imaging; RGB-D imaging; Stereo imaging; Deep learning; Point cloud analysis
Abstract
In recent years, three-dimensional (3D) machine vision techniques have been widely employed in agriculture and food systems, leveraging advanced deep learning technologies. However, with the rapid development of three-dimensional (3D) imaging techniques, the lack of a systematic review has hindered our ability to identify the most suitable imaging systems for specific agricultural and food applications. In this review, a variety of 3D imaging techniques are introduced, with their working principles and applications in agriculture and food systems. These techniques include Structure lighting-based 3D imaging, Multiview 3D imaging system, Time of Flight (ToF)-based 3D imaging system, Lighting Detection and Ranging (LiDAR), and Depth estimation from monocular image. Furthermore, the three-dimensional image analysis methods applied to these 3D imaging techniques are described and discussed in this review.
Citation
Xiang, L., & Wang, D. (2023). A Review of Three-Dimensional Vision Techniques in Food and Agriculture Applications. Smart Agricultural Technology, 5, 100259. https://doi.org/10.1016/j.atech.2023.100259
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
