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Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of...

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Autores principales: Ni, Jun, Yao, Lili, Zhang, Jingchao, Cao, Weixing, Zhu, Yan, Tai, Xiuxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375788/
https://www.ncbi.nlm.nih.gov/pubmed/28273815
http://dx.doi.org/10.3390/s17030502
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author Ni, Jun
Yao, Lili
Zhang, Jingchao
Cao, Weixing
Zhu, Yan
Tai, Xiuxiang
author_facet Ni, Jun
Yao, Lili
Zhang, Jingchao
Cao, Weixing
Zhu, Yan
Tai, Xiuxiang
author_sort Ni, Jun
collection PubMed
description In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R(2)) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.
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spelling pubmed-53757882017-04-10 Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System Ni, Jun Yao, Lili Zhang, Jingchao Cao, Weixing Zhu, Yan Tai, Xiuxiang Sensors (Basel) Article In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R(2)) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively. MDPI 2017-03-03 /pmc/articles/PMC5375788/ /pubmed/28273815 http://dx.doi.org/10.3390/s17030502 Text en © 2017 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ni, Jun
Yao, Lili
Zhang, Jingchao
Cao, Weixing
Zhu, Yan
Tai, Xiuxiang
Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
title Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
title_full Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
title_fullStr Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
title_full_unstemmed Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
title_short Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
title_sort development of an unmanned aerial vehicle-borne crop-growth monitoring system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375788/
https://www.ncbi.nlm.nih.gov/pubmed/28273815
http://dx.doi.org/10.3390/s17030502
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