This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria. Pollen images of 16 types:
images_16_types.zip
Acer Pseudoplatanus Aesculus Carnea Alnus Anthoxanthum Betula Pendula Brassica Carpinus Corylus Dactylis Glomerata Fraxinus Pinus Nigra Platanus Populus Nigra Prunus Avium Sequoiadendron Giganteum Taxus Baccata Pollen video library pollen_video_library.zip
Each type of pollen is in a separate folder, there may be multiple videos per type. In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1]. Field data over 3 days are gathered in Graz in spring 2020. pollen_field_data.zip
Sample code to load the data and visualize the images is in plot_pollen_sample.py
. Download and extract the file images_16_types.zip
in the same folder as plot_pollen_sample.py
to run the example. Dependecies opencv numpy matplotlib Credit [1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119. @inproceedings{namcao2020pollen, title = {Automated Pollen Detection with an Affordable Technology}, author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh}, booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)}, pages={108–119} month = {2}, year = {2020}, }