  Supervised learning is often used to count objects in images, but for
counting small, densely located objects, the required image annotations are
burdensome to collect. Counting plant organs for image-based plant phenotyping
falls within this category. Object counting in plant images is further
challenged by having plant image datasets with significant domain shift due to
different experimental conditions, e.g. applying an annotated dataset of indoor
plant images for use on outdoor images, or on a different plant species. In
this paper, we propose a domain-adversarial learning approach for domain
adaptation of density map estimation for the purposes of object counting. The
approach does not assume perfectly aligned distributions between the source and
target datasets, which makes it more broadly applicable within general object
counting and plant organ counting tasks. Evaluation on two diverse object
counting tasks (wheat spikelets, leaves) demonstrates consistent performance on
the target datasets across different classes of domain shift: from
indoor-to-outdoor images and from species-to-species adaptation.
