  The spread of the Red Pal Weevil (RPW) has become an existential threat for
palm trees around the world. In the Middle East, RPW is causing wide-spread
damage to date palm Phoenix dactylifera L., having both agricultural impacts on
the palm production and environmental impacts. Early detection of RPW is very
challenging, especially at large scale. This research proposes a novel remote
sensing approach to recognize and monitor red palm weevil in date palm trees,
using a combination of vegetation indices, object detection and semantic
segmentation techniques. The study area consists of date palm trees with three
classes, including healthy palms, smallish palms and severely infected palms.
This proposed method achieved a promising 0.947 F1 score on test data set. This
work paves the way for deploying artificial intelligence approaches to monitor
RPW in large-scale as well as provide guidance for practitioners.
