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Article

Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis

by 1, 1, 1,* and 2
1
College of Electrical and Information, Heilongjiang Bayi Agricultural University, DaQing 163319, China
2
College of Agronomy, Heilongjiang Bayi Agricultural University, DaQing 163319, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1206; https://doi.org/10.3390/rs10081206
Received: 11 June 2018 / Revised: 29 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R2) of 0.9890 for the estimation of soybean height and a R2 of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R2 of 0.9936 for the estimation of soybean height and a R2 of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions. View Full-Text
Keywords: soybean plant; 3D reconstruction; multisource imaging; phenotyping; plant height; Greenness soybean plant; 3D reconstruction; multisource imaging; phenotyping; plant height; Greenness
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MDPI and ACS Style

Guan, H.; Liu, M.; Ma, X.; Yu, S. Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis. Remote Sens. 2018, 10, 1206. https://doi.org/10.3390/rs10081206

AMA Style

Guan H, Liu M, Ma X, Yu S. Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis. Remote Sensing. 2018; 10(8):1206. https://doi.org/10.3390/rs10081206

Chicago/Turabian Style

Guan, Haiou, Meng Liu, Xiaodan Ma, and Song Yu. 2018. "Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis" Remote Sensing 10, no. 8: 1206. https://doi.org/10.3390/rs10081206

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