  Precision agriculture has become a key factor for increasing crop yields by
providing essential information to decision makers. In this work, we present a
deep learning method for simultaneous segmentation and counting of cranberries
to aid in yield estimation and sun exposure predictions. Notably, supervision
is done using low cost center point annotations. The approach, named Triple-S
Network, incorporates a three-part loss with shape priors to promote better
fitting to objects of known shape typical in agricultural scenes. Our results
improve overall segmentation performance by more than 6.74% and counting
results by 22.91% when compared to state-of-the-art. To train and evaluate the
network, we have collected the CRanberry Aerial Imagery Dataset (CRAID), the
largest dataset of aerial drone imagery from cranberry fields. This dataset
will be made publicly available.
