  Service robotics is recently enhancing precision agriculture enabling many
automated processes based on efficient autonomous navigation solutions.
However, data generation and infield validation campaigns hinder the progress
of large-scale autonomous platforms. Simulated environments and deep visual
perception are spreading as successful tools to speed up the development of
robust navigation with low-cost RGB-D cameras. In this context, the
contribution of this work is twofold: a synthetic dataset to train deep
semantic segmentation networks together with a collection of virtual scenarios
for a fast evaluation of navigation algorithms. Moreover, an automatic
parametric approach is developed to explore different field geometries and
features. The simulation framework and the dataset have been evaluated by
training a deep segmentation network on different crops and benchmarking the
resulting navigation.
