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Multispectral UAV remote sensing for the spatial differentiated assessment of crop yield

Norman Wilke

The rapid development in sensor technology and data processing contributes to the increasing digitalization in agriculture. Especially in precision farming the use of unmanned aerial vehicles (UAVs) can help to advance and accelerate this process to increase crop productivity and farm profitability, while reducing negative environmental impacts.

Different sensors (RGB, multispectral) mounted on UAVs in combination with 3D surface modeling (e.g., using structure for motion) offer the opportunity for the non-destructive and quantitative assessment of plant traits (e.g., emergence, fractional cover, leaf area index, plant height and biomass) with high spatial and temporal resolution. Furthermore, the precise characterization of wheat plants will help to improve the spatially differentiated prediction of crop yield.

The Ph.D. project is focused on the improvement of crop growth modelling for wheat by including remote sensing data acquired from UAVs. This allows yield forecasts in different stages of plant development and can help to further establish UAV remote sensing in precision farming and phenotyping.