Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton
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Author
Thompson, AlisonThorp, Kelly
Conley, Matthew
Elshikha, Diaa
French, Andrew
Andrade-Sanchez, Pedro
Pauli, Duke
Affiliation
Univ Arizona, Maricopa Agr CtrUniv Arizona, Sch Plant Sci
Issue Date
2019-03-23Keywords
cottonplant height
high-throughput phenotyping
ultrasonic transducers
unmanned aerial systems
light detection and ranging
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MDPICitation
Thompson AL, Thorp KR, Conley MM, Elshikha DM, French AN, Andrade-Sanchez P, Pauli D. Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton. Remote Sensing. 2019; 11(6):700.Journal
REMOTE SENSINGRights
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.Note
Open access journalISSN
2072-4292Version
Final published versionSponsors
Cotton Incorporated Research Grant [13-738]; United States Department of Agriculture-Agricultural Research Service [2020-21410-006-00D]ae974a485f413a2113503eed53cd6c53
10.3390/rs11060700
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Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

