Agriculture Reference
In-Depth Information
100
R
2
= 0.7275
95
90
85
80
75
70
150
170
190
210
230
250
270
Leaf area index (%)
FIGURE 2.27
Accuracy of estimating crop height by MSIS in a field.
content. A strong correlation of
R
2
= 0.8246 between MSIS response and chlorophyll-
a
content was obtained in the experiment. From this result, it was confirmed that the
MSIS could remotely detect the chlorophyll content. Height estimation performance
is illustrated in Figure 2.27. As described above, LAI could be used to estimate the
crop height. Although the height difference in tested images was relatively small, the
height estimator provided an indication of the crop height variability. A corn stress
map (Figure 2.28) was created by the robot tractor with the MSIS. Corn stresses
detected by the MSIS clearly corresponded with SPAD reading map.
2.9
MULTIPLE ROBOTS SYSTEM
2.9.1 C
ONCEPT
OF
M
ULTIPLE
R
OBOTS
As described in Section 2.5, robot vehicles using an RTK-GPS and an IMU have
already been completed. The robot vehicles have successfully performed the till-
age, planting, cultivating, spraying, fertilizing, and harvesting. A lateral error of the
robot's operation was less than 5 cm, which is an improvement over skilled-human
operation. During curved crop-row operations, the robot was able to navigate with-
out causing plant damage. However, when a robot is used in an open site (which is
the case in most practical operations) some type of monitoring system is required to
ensure safety (Reid
et al., 2000). Therefore, at least one farm manager is required
to supervise the robot during operation using a robot management system described
above. In order to improve efficiency, the next step for agricultural robot develop-
ment is to increase the number of robots in simultaneous operation by developing a
multiple robot system. To accomplish this objective, it is essential to develop mobile
robots with minimal centralized control. The solution to this decentralization prob-
lem is to give more autonomy to the robots to allow them to cope with unexpected
events and obstacles, such as other vehicles, having an inaccurate environment
model, and similar challenges. Such a high level of autonomy can be achieved using
