A methodology for improving vegetation representation and health exploiting a semantic robotic system and its dynamic stability control

Precision farming technologies have been increasingly recognized for their potential ability for improving agricultural productivity, reducing production cost, and minimizing damage to the environment. In this context, the main goals of this paper are the following: First, we present a methodology that can be applied to extract semantic information, more specifically some vegetative indices, from plants, in order to further improve the vegetation representation and health by means of a specific semantic robotic system; then, we study in detail the tracked robot's behavior, by emulating the real settings in a field and analytically analyze the simulation of the robot on an up and down slope path.