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Article

Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

by 1, 1,2,* and 2,3
1
Bio-Sensing and Instrumentation Laboratory, College of Engineering, The University of Georgia, Athens, GA WJXF 63, USA
2
Phenomics and Plant Robotics Center, The University of Georgia, Athens, GA WJXF 63, USA
3
Center for Geospatial Research, The University of Georgia, Athens, GA WJXF 63, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Kuniaki Uto, Nicola Falco and Mauro Dalla Mura
Remote Sens. 2021, 13(17), 3517; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173517
Received: 18 June 2021 / Revised: 27 August 2021 / Accepted: 29 August 2021 / Published: 4 September 2021
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management. View Full-Text
Keywords: UAV; thermal imaging; multispectral imaging; phenotyping UAV; thermal imaging; multispectral imaging; phenotyping
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MDPI and ACS Style

Xu, R.; Li, C.; Bernardes, S. Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture. Remote Sens. 2021, 13, 3517. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173517

AMA Style

Xu R, Li C, Bernardes S. Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture. Remote Sensing. 2021; 13(17):3517. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173517

Chicago/Turabian Style

Xu, Rui, Changying Li, and Sergio Bernardes. 2021. "Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture" Remote Sensing 13, no. 17: 3517. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173517

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