Drone based, or drone assisted phenotyping has been described in the literature for many crops and traits with generally high accuracies in comparison to field based methods. Homan et al (2016) found for example consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively) for drone based wheat height measurements, enabling crop growth rate to be derived from multi-temporal surface models. Also the drone industry has pointed towards agriculture as one of the leading industries for drone use. However, the practical implementation into the experimental fields of plant breeders or public research facilities has been limited to demonstrations and publicly funded research programs. The step towards fully exploiting the potential of drone based phenotyping requires (i) the simplification of data acquisition and data handling ,(ii) data analytics which are focussed to extract relevant agrometrics on micro-plot level and (iii) the integration within day-to-day workflows. Over the past 4 years, we have collaborated with plant breeders and public research facilities to develop MAPEO, a drone image processing workflow dedicated to experimental fields. Plant height for wheat and corn plots, diseases in sugar beets and wheat trials, plant emergence for potatoes and spinach are just a few traits for which image analytics were provided and which resulted in agrometrics which reaches or even surpassed field based measurements. Technologies like MAPEO are there to help make plant breeders to fully adopt and integrate drones into their day to day lives.