Towards automatic UAV data interpretation for precision farming

Background: The EU-project Flourish intends to establish an autonomously operating precision farming system based on the interaction between unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). For effective mission planning and site-specific ground intervention by the UGV, the growth, mineral nutrition, weed, and health status of the crop field must be evaluated efficiently. In this regard, the survey capabilities of UAVs can substantially leverage the economic performance and ecological sustainability of precision farming systems. Methods: Our approach is based on ‘sufficient performance ranges’ (SPRs), which represent the expected optimal performance of phenotypic traits such as spectral indicators and growth parameters like canopy cover and crop height. Together with a priori data, such as weather situation and soil fertility maps, the UAV derived maps allow for the detection of deviations from sufficient crop development. Detected deviations are interpreted using decision tree models. Our models encompass upstream and downstream decisions necessary for scheduling site-specific and efficient ground interventions during the whole management sequence of a growing season, such as fertilizer input or weed control. Results: This contribution presents initial results for field monitoring via UAVs. The performance of our approach is supported by ground truth data, such as crop height, canopy cover and spectral indices from sugar beets collected in 2015. Effects of variable soil fertility, weed pressure and drought stress are presented. Conclusion: The initial results support the proposed intention to derive appropriate management decisions for stabilizing crop yield and quality while minimizing farm inputs.