  Precision agriculture is a fast-growing field that aims at introducing
affordable and effective automation into agricultural processes. Nowadays,
algorithmic solutions for navigation in vineyards require expensive sensors and
high computational workloads that preclude large-scale applicability of
autonomous robotic platforms in real business case scenarios. From this
perspective, our novel proposed control leverages the latest advancement in
machine perception and edge AI techniques to achieve highly affordable and
reliable navigation inside vineyard rows with low computational and power
consumption. Indeed, using a custom-trained segmentation network and a
low-range RGB-D camera, we are able to take advantage of the semantic
information of the environment to produce smooth trajectories and stable
control in different vineyards scenarios. Moreover, the segmentation maps
generated by the control algorithm itself could be directly exploited as
filters for a vegetative assessment of the crop status. Extensive
experimentations and evaluations against real-world data and simulated
environments demonstrated the effectiveness and intrinsic robustness of our
methodology.
