  To optimize fruit production, a portion of the flowers and fruitlets of apple
trees must be removed early in the growing season. The proportion to be removed
is determined by the bloom intensity, i.e., the number of flowers present in
the orchard. Several automated computer vision systems have been proposed to
estimate bloom intensity, but their overall performance is still far from
satisfactory even in relatively controlled environments. With the goal of
devising a technique for flower identification which is robust to clutter and
to changes in illumination, this paper presents a method in which a pre-trained
convolutional neural network is fine-tuned to become specially sensitive to
flowers. Experimental results on a challenging dataset demonstrate that our
method significantly outperforms three approaches that represent the state of
the art in flower detection, with recall and precision rates higher than
$90\%$. Moreover, a performance assessment on three additional datasets
previously unseen by the network, which consist of different flower species and
were acquired under different conditions, reveals that the proposed method
highly surpasses baseline approaches in terms of generalization capability.
