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Proceedings Paper

Plant phenotyping using multi-view stereo vision with structured lights
Author(s): Thuy Tuong Nguyen; David C. Slaughter; Julin N. Maloof; Neelima Sinha
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Paper Abstract

A multi-view stereo vision system for true 3D reconstruction, modeling and phenotyping of plants was created that successfully resolves many of the shortcomings of traditional camera-based 3D plant phenotyping systems. This novel system incorporates several features including: computer algorithms, including camera calibration, excessive-green based plant segmentation, semi-global stereo block matching, disparity bilateral filtering, 3D point cloud processing, and 3D feature extraction, and hardware consisting of a hemispherical superstructure designed to hold five stereo pairs of cameras and a custom designed structured light pattern illumination system. This system is nondestructive and can extract 3D features of whole plants modeled from multiple pairs of stereo images taken at different view angles. The study characterizes the systems phenotyping performance for 3D plant features: plant height, total leaf area, and total leaf shading area. For plants having specified leaf spacing and size, the algorithms used in our system yielded satisfactory experimental results and demonstrated the ability to study plant development where the same plants were repeatedly imaged and phenotyped over the time.

Paper Details

Date Published: 17 May 2016
PDF: 9 pages
Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 986608 (17 May 2016); doi: 10.1117/12.2229513
Show Author Affiliations
Thuy Tuong Nguyen, Univ. of California, Davis (United States)
David C. Slaughter, Univ. of California, Davis (United States)
Julin N. Maloof, Univ. of California, Davis (United States)
Neelima Sinha, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 9866:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
John Valasek; J. Alex Thomasson, Editor(s)

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