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Sensors 2018, 18(7), 2270; https://doi.org/10.3390/s18072270

Evaluating Geometric Measurement Accuracy Based on 3D Reconstruction of Automated Imagery in a Greenhouse

1
Division of Food Systems and Bioengineering, University of Missouri, Columbia, MO 65211, USA
2
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
*
Author to whom correspondence should be addressed.
Received: 10 May 2018 / Revised: 8 July 2018 / Accepted: 12 July 2018 / Published: 13 July 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method. View Full-Text
Keywords: 3D model reconstruction; structure from motion; geometric accuracy; processing efficiency; high-throughput phenotyping 3D model reconstruction; structure from motion; geometric accuracy; processing efficiency; high-throughput 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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Zhou, J.; Fu, X.; Schumacher, L.; Zhou, J. Evaluating Geometric Measurement Accuracy Based on 3D Reconstruction of Automated Imagery in a Greenhouse. Sensors 2018, 18, 2270.

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