TY - JOUR
T1 - A novel LiDAR-Based instrument for high-throughput, 3D measurement of morphological traits in maize and sorghum
AU - Thapa, Suresh
AU - Zhu, Feiyu
AU - Walia, Harkamal
AU - Yu, Hongfeng
AU - Ge, Yufeng
N1 - Funding Information:
The funding for this work was provided by the National Science Foundation (DBI-1556186). The authors would like to acknowledge the staff members at the University of Nebraska-Lincoln’s Greenhouse Innovation Center for their assistance in data collection.
Funding Information:
The funding for this work was provided by the National Science Foundation (DBI-1556186). The authors would like to acknowledge the staff members at the University of Nebraska-Lincoln?s Greenhouse Innovation Center for their assistance in data collection.
Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/4/13
Y1 - 2018/4/13
N2 - Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 > 0.91 for individual leaf area; R2 > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.
AB - Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 > 0.91 for individual leaf area; R2 > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.
KW - 3D point cloud
KW - High-throughput plant phenotyping
KW - LIDAR
KW - Leaf angular distribution
KW - Leaf area
KW - Leaf inclination angle
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U2 - 10.3390/s18041187
DO - 10.3390/s18041187
M3 - Article
C2 - 29652788
AN - SCOPUS:85045475529
VL - 18
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
SN - 1424-3210
IS - 4
M1 - 1187
ER -