  Advances in precision agriculture greatly rely on innovative control and
sensing technologies that allow service units to increase their level of
driving automation while ensuring at the same time high safety standards. This
paper deals with automatic terrain estimation and classification that is
performed simultaneously by an agricultural vehicle during normal operations.
Vehicle mobility and safety, and the successful implementation of important
agricultural tasks including seeding, ploughing, fertilising and controlled
traffic depend or can be improved by a correct identification of the terrain
that is traversed. The novelty of this research lies in that terrain estimation
is performed by using not only traditional appearance-based features, that is
colour and geometric properties, but also contact-based features, that is
measuring physics-based dynamic effects that govern the vehicleeterrain
interaction and that greatly affect its mobility. Experimental results obtained
from an all-terrain vehicle operating on different surfaces are presented to
validate the system in the field. It was shown that a terrain classifier
trained with contact features was able to achieve a correct prediction rate of
85.1%, which is comparable or better than that obtained with approaches using
traditional feature sets. To further improve the classification performance,
all feature sets were merged in an augmented feature space, reaching, for these
tests, 89.1% of correct predictions.
