  Severe weather events can cause large financial losses to farmers. Detailed
information on the location and severity of damage will assist farmers,
insurance companies, and disaster response agencies in making wise post-damage
decisions. The goal of this study was a proof-of-concept to detect damaged crop
areas from aerial imagery using computer vision and deep learning techniques. A
specific objective was to compare existing object detection algorithms to
determine which was best suited for crop damage detection. Two modes of crop
damage common in maize (corn) production were simulated: stalk lodging at the
lowest ear and stalk lodging at ground level. Simulated damage was used to
create a training and analysis data set. An unmanned aerial system (UAS)
equipped with a RGB camera was used for image acquisition. Three popular object
detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability
to detect damaged regions in a field. Average precision was used to compare
object detectors. YOLOv2 and RetinaNet were able to detect crop damage across
multiple late-season growth stages. Faster R-CNN was not successful as the
other two advanced detectors. Detecting crop damage at later growth stages was
more difficult for all tested object detectors. Weed pressure in simulated
damage plots and increased target density added additional complexity.
