  This paper describes a computationally-enhanced M100 UAV platform with an
onboard deep learning inference system for integrated computer vision and
navigation able to autonomously find and visually identify by coat pattern
individual Holstein Friesian cattle in freely moving herds. We propose an
approach that utilises three deep convolutional neural network architectures
running live onboard the aircraft; that is, a YoloV2-based species detector, a
dual-stream CNN delivering exploratory agency and an InceptionV3-based
biometric LRCN for individual animal identification. We evaluate the
performance of each of the components offline, and also online via real-world
field tests comprising 146.7 minutes of autonomous low altitude flight in a
farm environment over a dispersed herd of 17 heifer dairy cows. We report
error-free identification performance on this online experiment. The presented
proof-of-concept system is the first of its kind and a successful step towards
autonomous biometric identification of individual animals from the air in open
pasture environments for tag-less AI support in farming and ecology.
