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Sensors 2015, 15(8), 18587-18612; doi:10.3390/s150818587

Structured Light-Based 3D Reconstruction System for Plants

Department of Computer Science, University of California, Davis, CA 95616, USA
Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA
Department of Plant Biology, University of California, Davis, CA 95616, USA
Authors to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 11 June 2015 / Revised: 21 July 2015 / Accepted: 24 July 2015 / Published: 29 July 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3367 KB, uploaded 29 July 2015]   |  


Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance. View Full-Text
Keywords: plant phenotyping; 3D reconstruction; stereo vision; structured light; point cloud; 3D feature extraction plant phenotyping; 3D reconstruction; stereo vision; structured light; point cloud; 3D feature extraction

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nguyen, T.T.; Slaughter, D.C.; Max, N.; Maloof, J.N.; Sinha, N. Structured Light-Based 3D Reconstruction System for Plants. Sensors 2015, 15, 18587-18612.

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