Biography

I am a researcher and expert for utilizing unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) for sensor measurements in agriculture. I focus amongst others on hyperspectral remote sensing, photogrammetry, image processing, geo-information, programming, prototyping, and multivariate statistics. My field of research comprises both grain and forage production.

CV

Competences: 
Multi- and hyperspectral remote sensing in agriculture, UAV, UGV, GNSS, GIS, sensor web, mapping, 3D modelling, multivariate and geo-statistics, programming, prototyping

Education: 
2012-2016: Dr. sc. agr. (Ph.D.) in Agricultural Sciences at the Institute of Crop Science, Department of Agronomy, University of Hohenheim, Germany

2009-2012: M.Sc. in Geoinformatics at the Institute for Geoinformatics, University of Münster, Germany

2005-2009: Dipl.-Ing. (FH) in Surveying Engineering and Geoinformatics at the University of Applied Sciences Würzburg-Schweinfurt, Germany

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Abstract

Today’s modern precision agriculture applications have a huge demand for data with high spatial and temporal resolution. This leads to the need of unmanned aerial vehicles (UAV) as sensor platforms providing both, easy use and a high area coverage. This study shows the successful development of a prototype hybrid UAV for practical applications in precision agriculture. The UAV consists of an off-the-shelf fixed-wing fuselage, which has been enhanced with multi-rotor functionality. It was programmed to perform pre-defined waypoint missions completely autonomously, including vertical take-off, horizontal flight, and vertical landing. The UAV was tested for its return-to-home (RTH) accuracy, power consumption and general flight performance at different wind speeds. The RTH accuracy was 43.7 cm in average, with a root-mean-square error of 39.9 cm. The power consumption raised with an increase in wind speed. An extrapolation of the analysed power consumption to conditions without wind resulted in an estimated 40 km travel range, when we assumed a 25 % safety margin of remaining battery capacity. This translates to a maximal area coverage of 300 ha for a scenario with 18 m/s airspeed, 50 minutes flight time, 120 m AGL altitude, and a desired 70 % of image side-lap and 85 % forward-lap. The ground sample distance with an in-built RGB camera was 3.5 cm, which we consider sufficient for farm-scale mapping missions for most precision agriculture applications.

Abstract

In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.