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J. Imaging 2017, 3(3), 39;

Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping

Arkansas Biosciences Institute, Arkansas State University, P.O. Box 639, State University, AR 72467, USA
Department of Computer Science, Arkansas State University, Jonesboro, AR 72401, USA
Department of Chemistry and Physics, Arkansas State University, P.O. Box 419, State University, AR 72467, USA
Author to whom correspondence should be addressed.
Received: 31 July 2017 / Revised: 31 August 2017 / Accepted: 7 September 2017 / Published: 18 September 2017
(This article belongs to the Special Issue 2D, 3D and 4D Imaging for Plant Phenotyping)
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Accurate high-resolution three-dimensional (3D) models are essential for a non-invasive analysis of phenotypic characteristics of plants. Previous limitations in 3D computer vision algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present an image-based 3D plant reconstruction system that can be achieved by using a single camera and a rotation stand. Our method is based on the structure from motion method, with a SIFT image feature descriptor. In order to improve the quality of the 3D models, we segmented the plant objects based on the PlantCV platform. We also deducted the optimal number of images needed for reconstructing a high-quality model. Experiments showed that an accurate 3D model of the plant was successfully could be reconstructed by our approach. This 3D surface model reconstruction system provides a simple and accurate computational platform for non-destructive, plant phenotyping. View Full-Text
Keywords: 3D surface model reconstruction; plant phenotyping; phenomics; Arabidopsis phenotyping 3D surface model reconstruction; plant phenotyping; phenomics; Arabidopsis phenotyping

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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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Liu, S.; Acosta-Gamboa, L.M.; Huang, X.; Lorence, A. Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping. J. Imaging 2017, 3, 39.

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