  Expensive sensors and inefficient algorithmic pipelines significantly affect
the overall cost of autonomous machines. However, affordable robotic solutions
are essential to practical usage, and their financial impact constitutes a
fundamental requirement to employ service robotics in most fields of
application. Among all, researchers in the precision agriculture domain strive
to devise robust and cost-effective autonomous platforms in order to provide
genuinely large-scale competitive solutions. In this article, we present a
complete algorithmic pipeline for row-based crops autonomous navigation,
specifically designed to cope with low-range sensors and seasonal variations.
Firstly, we build on a robust data-driven methodology to generate a viable path
for the autonomous machine, covering the full extension of the crop with only
the occupancy grid map information of the field. Moreover, our solution
leverages on latest advancement of deep learning optimization techniques and
synthetic generation of data to provide an affordable solution that efficiently
tackles the well-known Global Navigation Satellite System unreliability and
degradation due to vegetation growing inside rows. Extensive experimentation
and simulations against computer-generated environments and real-world crops
demonstrated the robustness and intrinsic generalizability of our methodology
that opens the possibility of highly affordable and fully autonomous machines.
