  Counting plant organs such as heads or tassels from outdoor imagery is a
popular benchmark computer vision task in plant phenotyping, which has been
previously investigated in the literature using state-of-the-art supervised
deep learning techniques. However, the annotation of organs in field images is
time-consuming and prone to errors. In this paper, we propose a fully
unsupervised technique for counting dense objects such as plant organs. We use
a convolutional network-based unsupervised segmentation method followed by two
post-hoc optimization steps. The proposed technique is shown to provide
competitive counting performance on a range of organ counting tasks in sorghum
(S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or
modifications.
