  In precision crop protection, (target-orientated) object detection in image
processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection
drones) to the right place to apply the pesticide. Unnecessary application of
non-target areas could be avoided. Deep learning algorithms dominantly use in
modern computer vision tasks which require high computing time, memory
footprint, and power consumption. Based on the Edge Artificial Intelligence, we
investigate the main three paths that lead to dealing with this problem,
including hardware accelerators, efficient algorithms, and model compression.
Finally, we integrate them and propose a solution based on a light deep neural
network (DNN), called Ag-YOLO, which can make the crop protection UAV have the
ability to target detection and autonomous operation. This solution is
restricted in size, cost, flexible, fast, and energy-effective. The hardware is
only 18 grams in weight and 1.5 watts in energy consumption, and the developed
DNN model needs only 838 kilobytes of disc space. We tested the developed
hardware and software in comparison to the tiny version of the state-of-art
YOLOv3 framework, known as YOLOv3-Tiny to detect individual palm in a
plantation. An average F1 score of 0.9205 at the speed of 36.5 frames per
second (in comparison to similar accuracy at 18 frames per second and 8.66
megabytes of the YOLOv3-Tiny algorithm) was reached. This developed detection
system is easily plugged into any machines already purchased as long as the
machines have USB ports and run Linux Operating System.
