Viertel, PhilippKoenig, MatthiasRexilius, JanGandorfer, MarkusHoffmann, ChristaEl Benni, NadjaCockburn, MarianneAnken, ThomasFloto, Helga2022-02-242022-02-242022978-3-88579-711-1https://dl.gi.de/handle/20.500.12116/38415This paper deals with the localization and classification of pollen grains in light-microscopic images from pollen samples and honey sediments. A laboratory analysis of the honey sediment offers precise information of the honey composition. By utilizing state of the art deep neural networks, we show the possibility of automatizing the process of pollen counting and identification. For that purpose, we created and labelled our own data set comprising two pollen classes and trained and evaluated a regional-based neural network. Our results show that the majority of pollen grains are correctly detected. The pollen frequency in the honey sediment is on par with the majority pollen class, however, more samples and further investigation are required to ensure stable results and practicality.enDeep Learningpalynologymelissopalynologypollen analysisobject detectionText/Conference Paper1617-5468