  In fruit production, critical crop management decisions are guided by bloom
intensity, i.e., the number of flowers present in an orchard. Despite its
importance, bloom intensity is still typically estimated by means of human
visual inspection. Existing automated computer vision systems for flower
identification are based on hand-engineered techniques that work only under
specific conditions and with limited performance. This work proposes an
automated technique for flower identification that is robust to uncontrolled
environments and applicable to different flower species. Our method relies on
an end-to-end residual convolutional neural network (CNN) that represents the
state-of-the-art in semantic segmentation. To enhance its sensitivity to
flowers, we fine-tune this network using a single dataset of apple flower
images. Since CNNs tend to produce coarse segmentations, we employ a refinement
method to better distinguish between individual flower instances. Without any
pre-processing or dataset-specific training, experimental results on images of
apple, peach and pear flowers, acquired under different conditions demonstrate
the robustness and broad applicability of our method.
