Towards automatic UAV data interpretation for precision farming
Background: The EU project Flourish intends to esta blish an autonomously operating precision farming s ystem based on the interaction between unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) For ef fective mission planning and site specific ground intervent ion by the UGV, the growth, mineral nutrition, weed and health status of the crop field must be evaluated efficien tly. In this regard, the survey capabilities of UAV s can substantially leverage the economic performance and ecological su stainability of precision farming systems. Methods: Our approach is based on 'sufficient perfo rmance ranges' (SPRs) which represent the expected optimal performance of phenotypic traits such as spectral i ndicators and growth parameters like canopy cover a nd 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 develo pment. Detected deviations are interpreted using de cision tree models. Our models encompass upstream and downstream decisi ons necessary for scheduling site specific and effi ci nt ground interventions during the whole management sequence of a growing season, such as fertilizer input or we ed control. Results: This contribution presents initial results for field monitoring via UAVs. The performance of ur approach is supported by ground truth data, such as crop height canopy cover and spectral indices from sugar beet s collected in 2015. Effects of variable soil fertility, weed pres sure and drought stress are presented. Conclusion: The initial results support the propose d intention to derive appropriate management decisi ons for stabilizing crop yield and quality while minimizing farm inputs.