  Reducing the use of agrochemicals is an important component towards
sustainable agriculture. Robots that can perform targeted weed control offer
the potential to contribute to this goal, for example, through specialized
weeding actions such as selective spraying or mechanical weed removal. A
prerequisite of such systems is a reliable and robust plant classification
system that is able to distinguish crop and weed in the field. A major
challenge in this context is the fact that different fields show a large
variability. Thus, classification systems have to robustly cope with
substantial environmental changes with respect to weed pressure and weed types,
growth stages of the crop, visual appearance, and soil conditions. In this
paper, we propose a novel crop-weed classification system that relies on a
fully convolutional network with an encoder-decoder structure and incorporates
spatial information by considering image sequences. Exploiting the crop
arrangement information that is observable from the image sequences enables our
system to robustly estimate a pixel-wise labeling of the images into crop and
weed, i.e., a semantic segmentation. We provide a thorough experimental
evaluation, which shows that our system generalizes well to previously unseen
fields under varying environmental conditions --- a key capability to actually
use such systems in precision framing. We provide comparisons to other
state-of-the-art approaches and show that our system substantially improves the
accuracy of crop-weed classification without requiring a retraining of the
model.
