期刊
SENSORS
卷 17, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/s17030502
关键词
unmanned aerial vehicle sensor; crop-growth model; computational fluid dynamics; flow field analysis; monitoring system; field experiment
资金
- National Natural Science Foundation of China [31371534]
- Primary Research & Development Plan of Jiangsu Province of China [BE2016378]
- National Key Research and Development Program of China [2016YFD0300606]
- Jiangsu Agricultural Science and Technology Independent Innovation Fund Project [CX(16)1006]
- The Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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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