Agriculture Reference
In-Depth Information
assure the acknowledgment of potential hazards around the vehicle, regardless of
the presence of a watchman in the cabin. Wexler (2006) proved that professionals
whose work requires long hours alone in monotonous sensory environments suffer
from alterations in mood, perception, and cognition resulting from extended peri-
ods of decreased sensory stimulation. Computer assistance for driving tractors and
combines over the long working days of harvesting seasons may substantially reduce
the mental fatigue of vehicle operators, and as a result, the likelihood of accidents.
Figure 12.1 depicts a conceptual chart with several examples of the perceptual needs
of intelligent agricultural vehicles.
Although the technology used for perceiving from aerial vehicles shares most of
the technology used for land vehicles, the challenges and solutions substantially dif-
fer. The creation of three-dimensional (3-D) crop maps based on aerial stereoscopic
images (Rovira-Más et al. 2005), for instance, is very sensitive to the accuracy in
the estimation of the distance from the camera to the ground; the generation of 3-D
terrain maps, on the contrary, highly depends on the correct measurement of the
vehicle's heading (Rovira-Más et al. 2008). It is essential, therefore, to focus on the
type of platform selected. At this point, the presence of aircraft—either manned or
remote-controlled—for private use in conventional farms is symbolic, and for that
reason this chapter will mainly concentrate on terrestrial off-road equipment such as
tractors, sprayers, harvesters, and scouting utility vehicles. Once vehicle and mission
have been determined, some factors need to be discussed about the environments
expected in agricultural fields. Generally speaking, we can classify agricultural
environments as
semistructured
. Even though there will be situations in which auto-
mated vehicles will need to operate in barren fields with the purpose of terrain con-
ditioning and planting, most of the times farm equipment has to navigate within
Crop production status
NIR reflectance
plant vigor
hermography
water stress
Multispectral vision
plant disease
Navigation and positioning
NIR monocular
crop guidance
Stereo vision
3D mapping
Stereo vision
obstacle detection
FIGURE 12.1
Perceptual needs of intelligent agricultural vehicles.
