Embedded Control for Improved Pollen Detection and Classification

Automatic pollen sensing is important to understand the local distribution of pollen in urban environments and to give personalized advice to the citizens suffering from seasonal pollen allergies to help milder the symptoms.

You will control the focal plane of the camera module in real time to improve the outcome of the pollen identification models and minimize the amount of data to be processed.

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Student Target Groups:

  • Students in ICE and Computer Science.

Thesis Type:

  • Master Project / Master Thesis

Goals and tasks:

  • Literature review on control of the focus plane to improve the outcome of object detection and classification tasks.
  • Given pollen identification models trained on library data, design an algorithm to predict and choose the focal plane setting to optimize system performance.
  • Test your embedded control algorithm on a live system (remote access will be provided)
  • Summarize the results in a written report

Recommended Prior Knowledge:

  • Experience with embedded systems and basic knowledge of deep learning models, creative thinking.
  • Joy working with real systems and real data
  • Programming skills in Python


  • As soon as possible