  As herbarium specimens are increasingly becoming digitized and accessible in
online repositories, advanced computer vision techniques are being used to
extract information from them. The presence of certain plant organs on
herbarium sheets is useful information in various scientific contexts and
automatic recognition of these organs will help mobilize such information. In
our study we use deep learning to detect plant organs on digitized herbarium
specimens with Faster R-CNN. For our experiment we manually annotated hundreds
of herbarium scans with thousands of bounding boxes for six types of plant
organs and used them for training and evaluating the plant organ detection
model. The model worked particularly well on leaves and stems, while flowers
were also present in large numbers in the sheets, but not equally well
recognized.
