Studentinformatie

Msc thesis subject: Crop parameter estimation with UAV laser scanning

In the context of precision agriculture, monitoring crop conditions with Unmanned Aerial Vehicles (UAVs, drones) has been proposed as a cost-efficient and timely option. Additionally, UAVs typically offer centimetre resolution observations necessary for precise monitoring. The increasing availability of multi-spectral imaging systems that can be operated out-of-the-box has led to a multitude of applications. Acquisition protocols and standards are only catching up. However, the calibration quality of passive imaging sensors is dependent on cloud conditions during acquisition, which compromises flexibility. Active sensors like light detection and ranging (lidar) are illumination independent and additionally offer accurate geometrical information of the target.

The GRS group has a UAV-lidar system available, the Riegl RiCopter with VUX1. So far, applications in forestry, terrain monitoring, production of elevation models and habitat monitoring have been tested. Crop parameter estimation for precision agriculture is still to be explored. For 2019, a field campaign is planned in which comprehensive crop parameters will be collected (height, leaf area, leaf angle) for different crops. The goal of this thesis is to explore how the UAV-lidar can be used to estimate these crop parameters.

Objectives

  • Review literature on precision agriculture with UAVs with focus on geometrical parameters (leaf area, height, leaf angle)
  • Prepare the different datasets (UAV-lidar, crop parameters) for analysis
  • Test different approaches to estimate crop parameters (empirical and model based)

Literature

Requirements

  • Basic scripting skills (e.g. R, Python, MatLab) (more will be learned during the thesis)

Theme(s): Sensing & measuring