  Bark beetle outbreaks can dramatically impact forest ecosystems and services
around the world. For the development of effective forest policies and
management plans, the early detection of infested trees is essential. Despite
the visual symptoms of bark beetle infestation, this task remains challenging,
considering overlapping tree crowns and non-homogeneity in crown foliage
discolouration. In this work, a deep learning based method is proposed to
effectively classify different stages of bark beetle attacks at the individual
tree level. The proposed method uses RetinaNet architecture (exploiting a
robust feature extraction backbone pre-trained for tree crown detection) to
train a shallow subnetwork for classifying the different attack stages of
images captured by unmanned aerial vehicles (UAVs). Moreover, various data
augmentation strategies are examined to address the class imbalance problem,
and consequently, the affine transformation is selected to be the most
effective one for this purpose. Experimental evaluations demonstrate the
effectiveness of the proposed method by achieving an average accuracy of
98.95%, considerably outperforming the baseline method by approximately 10%.
