  Over the last few years, the number of precision farming projects has
increased specifically in harvesting robots and many of which have made
continued progress from identifying crops to grasping the desired fruit or
vegetable. One of the most common issues found in precision farming projects is
that successful application is heavily dependent not just on identifying the
fruit but also on ensuring that localisation allows for accurate navigation.
These issues become significant factors when the robot is not operating in a
prearranged environment, or when vegetation becomes too thick, thus covering
crop. Moreover, running a state-of-the-art deep learning algorithm on an
embedded platform is also very challenging, resulting most of the times in low
frame rates. This paper proposes using the You Only Look Once version 3
(YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image
processing techniques for the application of precision farming robots targeting
strawberry detection, accelerated on a heterogeneous multiprocessor platform.
The results show a performance acceleration by five times when implemented on a
Field-Programmable Gate Array (FPGA) when compared with the same algorithm
running on the processor side with an accuracy of 78.3\% over the test set
comprised of 146 images.
