Next Article in Journal
Detection of Absorbing Aerosol Using Single Near-UV Radiance Measurements from a Cloud and Aerosol Imager
Previous Article in Journal
Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data
Article

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

1
College of Engineering, University of Georgia, Athens, GA 30602, USA
2
College of Agricultural and Environmental Sciences and Franklin College of Arts and Science, University of Georgia, Athens, GA 30602, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy, Clement Atzberger and Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 377; https://doi.org/10.3390/rs9040377
Received: 26 January 2017 / Revised: 30 March 2017 / Accepted: 13 April 2017 / Published: 18 April 2017
A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5° angular resolution and results showed an R2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan. View Full-Text
Keywords: precision agriculture; field robotics; LiDAR; high-throughput phenotyping; crop surface model; plant height precision agriculture; field robotics; LiDAR; high-throughput phenotyping; crop surface model; plant height
Show Figures

Graphical abstract

MDPI and ACS Style

Sun, S.; Li, C.; Paterson, A.H. In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR. Remote Sens. 2017, 9, 377. https://doi.org/10.3390/rs9040377

AMA Style

Sun S, Li C, Paterson AH. In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR. Remote Sensing. 2017; 9(4):377. https://doi.org/10.3390/rs9040377

Chicago/Turabian Style

Sun, Shangpeng, Changying Li, and Andrew H. Paterson. 2017. "In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR" Remote Sensing 9, no. 4: 377. https://doi.org/10.3390/rs9040377

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop