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Auflistung nach Autor:in "Viertel, Philipp"

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  • Konferenzbeitrag
    Deep Learning in palynology
    (41. GIL-Jahrestagung, Informations- und Kommunikationstechnologie in kritischen Zeiten, 2021) Viertel, Philipp; König, Matthias
    In this work, we will show a use case for visual pollen classification from honey samples. We discuss the current state of the art in pollen analysis, highlight the importance of data quantity and quality, and elaborate on how to transfer promising Deep Learning methods to the analysis of honey samples. A first experiment with a public data set is shown as well as samples from our work-in-progress data set. Our recommendations and methods show which steps are necessary in order to successfully deploy an automated pollen analysis solution for honey products.
  • Konferenzbeitrag
    Pollen detection from honey sediments via Region-Based Convolutional Neural Networks
    (42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Viertel, Philipp; Koenig, Matthias; Rexilius, Jan
    This 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.