Designing and Testing a UAV Mapping System for Agricultural Field Surveying

Area of Science:

• Agricultural Engineering
• Remote Sensing
• Geospatial Analysis

Background:

• Accurate crop biomass estimation is crucial for precision agriculture.
• Unmanned Aerial Vehicles (UAVs) equipped with Light Detection and Ranging (LiDAR) offer a promising method for high-resolution environmental mapping.
• Integrating data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors enhances the accuracy of UAV-based mapping.

Purpose of the Study:

• To develop and evaluate a sensory UAV setup for detailed mapping and analysis of agricultural fields.
• To correlate LiDAR-derived crop height measurements with varying nitrogen treatments in winter wheat.
• To assess the impact of different flight patterns on LiDAR-based crop volume estimation.

Main Methods:

• LiDAR data acquisition using a UAV, combined with GNSS and IMU sensor data.
• Environment mapping and point cloud generation utilizing the Robot Operating System (ROS) and Point Cloud Library (PCL).
• Crop volume estimation using a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m.

Main Results:

• Crop height estimates (0.35-0.58 m) showed a correlation with nitrogen application rates (0-300 kg N ha⁻¹).
• LiDAR mapping successfully characterized the winter wheat field, providing detailed point cloud data.
• Analysis indicated that flight patterns influence the accuracy of crop volume estimations.

Conclusions:

• UAV-LiDAR systems provide a viable tool for precise agricultural field mapping and biomass assessment.
• The proposed methodology enables accurate correlation between crop characteristics and agricultural inputs like nitrogen.
• Further optimization of flight patterns can enhance the reliability of UAV-based crop volume estimations.