  Agricultural robots are expected to increase yields in a sustainable way and
automate precision tasks, such as weeding and plant monitoring. At the same
time, they move in a continuously changing, semi-structured field environment,
in which features can hardly be found and reproduced at a later time.
Challenges for Lidar and visual detection systems stem from the fact that
plants can be very small, overlapping and have a steadily changing appearance.
Therefore, a popular way to localize vehicles with high accuracy is based on
ex- pensive global navigation satellite systems and not on natural landmarks.
The contribution of this work is a novel image- based plant localization
technique that uses the time-invariant stem emerging point as a reference. Our
approach is based on a fully convolutional neural network that learns landmark
localization from RGB and NIR image input in an end-to-end manner. The network
performs pose regression to generate a plant location likelihood map. Our
approach allows us to cope with visual variances of plants both for different
species and different growth stages. We achieve high localization accuracies as
shown in detailed evaluations of a sugar beet cultivation phase. In experiments
with our BoniRob we demonstrate that detections can be robustly reproduced with
centimeter accuracy.
