  To enable robotic weed control, we develop algorithms to detect nutsedge weed
from bermudagrass turf. Due to the similarity between the weed and the
background turf, manual data labeling is expensive and error-prone.
Consequently, directly applying deep learning methods for object detection
cannot generate satisfactory results. Building on an instance detection
approach (i.e. Mask R-CNN), we combine synthetic data with raw data to train
the network. We propose an algorithm to generate high fidelity synthetic data,
adopting different levels of annotations to reduce labeling cost. Moreover, we
construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural
network input to reduce the reliance on pixel-wise precise labeling. We also
modify loss function from cross entropy to Kullback-Leibler divergence which
accommodates uncertainty in the labeling process. We implement the proposed
algorithm and compare it with both Faster R-CNN and Mask R-CNN. The results
show that our design can effectively overcome the impact of imprecise and
insufficient training sample issues and significantly outperform the Faster
R-CNN counterpart with a false negative rate of only 0.4%. In particular, our
approach also reduces labeling time by 95% while achieving better performance
if comparing with the original Mask R-CNN approach.
