Identification of herbarium specimen sheet components from high-resolution images using deep learning

Abstract

Advanced computer vision techniques hold the potential to mobilise vast quantities of biodiversity data by facilitating the rapid extraction of text- and trait-based data from herbarium specimen digital images, and to increase the efficiency and accuracy of downstream data capture during digitisation. This investigation developed an object detection model using YOLOv5 and digitised collection images from the University of Melbourne Herbarium (MELU). The MELU-trained ‘sheet-component’ model—trained on 3371 annotated images, validated on 1000 annotated images, run using ‘large’ model type, at 640 pixels, for 200 epochs—successfully identified most of the 11 component types of the digital speci..