  This work presents an Artificial Intelligence (AI) system, based on the
Faster Region-Based Convolution Neural Network (Faster R-CNN) framework, which
detects and counts apples from oblique, aerial drone imagery of giant
commercial orchards. To reduce computational cost, a novel precursory stage to
the network is designed to preprocess raw imagery into cropped images of
individual trees. Unique geospatial identifiers are allocated to these using
the perspective projection model. This employs Real-Time Kinematic (RTK) data,
Digital Terrain and Surface Models (DTM and DSM), as well as internal and
external camera parameters. The bulk of experiments however focus on tuning
hyperparameters in the detection network itself. Apples which are on trees and
apples which are on the ground are treated as separate classes. A mean Average
Precision (mAP) metric, calibrated by the size of the two classes, is devised
to mitigate spurious results. Anchor box design is of key interest due to the
scale of the apples. As such, a k-means clustering approach, never before seen
in literature for Faster R-CNN, resulted in the most significant improvements
to calibrated mAP. Other experiments showed that the maximum number of box
proposals should be 225; the initial learning rate of 0.001 is best applied to
the adaptive RMS Prop optimiser; and ResNet 101 is the ideal base feature
extractor when considering mAP and, to a lesser extent, inference time. The
amalgamation of the optimal hyperparameters leads to a model with a calibrated
mAP of 0.7627.
