  Agricultural robots have the prospect to enable more efficient and
sustainable agricultural production of food, feed, and fiber. Perception of
crops and weeds is a central component of agricultural robots that aim to
monitor fields and assess the plants as well as their growth stage in an
automatic manner. Semantic perception mostly relies on deep learning using
supervised approaches, which require time and qualified workers to label fairly
large amounts of data. In this paper, we look into the problem of reducing the
amount of labels without compromising the final segmentation performance. For
robots operating in the field, pre-training networks in a supervised way is
already a popular method to reduce the number of required labeled images. We
investigate the possibility of pre-training in a self-supervised fashion using
data from the target domain. To better exploit this data, we propose a set of
domain-specific augmentation strategies. We evaluate our pre-training on
semantic segmentation and leaf instance segmentation, two important tasks in
our domain. The experimental results suggest that pre-training with
domain-specific data paired with our data augmentation strategy leads to
superior performance compared to commonly used pre-trainings. Furthermore, the
pre-trained networks obtain similar performance to the fully supervised with
less labeled data.
