研究目的
Developing a UAV-based LiDAR system to acquire accurate time-series 3D point clouds for monitoring plant height and canopy cover, integral for enhancing crop genetic improvement.
研究成果
UAV-based LiDAR data is a feasible and highly efficient method for conducting high throughput phenotyping, as demonstrated by the preliminary results for plant height and canopy cover estimation.
研究不足
The accuracy of the acquired information is contingent upon the integration of various system components and their calibration. Future work includes improving canopy cover estimation by accounting for areas where destructive sampling is done.
1：Experimental Design and Method Selection:
Development of a LiDAR-based mobile mapping system by selecting and integrating hardware components suitable for UAV-based high throughput phenotyping, followed by a novel calibration approach.
2：Sample Selection and Data Sources:
Agricultural field at the Agronomy Center for Research and Education (ACRE), Purdue University, with different genotypes of Sorghum.
3：List of Experimental Equipment and Materials:
DJI M600 UAV platform, Applanix APX-15 UAV POS unit, Sony alpha 7R camera, Velodyne VLP-16 Puck HI-RES LiDAR unit, Raspberry Pi
4：Experimental Procedures and Operational Workflow:
System integration and calibration, data acquisition over agricultural fields, and estimation of plant height and canopy cover using LiDAR data.
5：Data Analysis Methods:
Generation of Crop Surface Models (CSMs) and Digital Terrain Models (DTMs), and canopy cover estimation.
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