Agriculture Reference
In-Depth Information
The precise survey system was developed by measuring the already measured posi-
tion to identify the offset due to misalignment of sensor attachment. The GDS bias,
a significant error included by the direction data due to the influence of a magnetic
field surrounding the GDS, was compensated for by a fiber optic gyro. Finally, the
field test was executed to evaluate the accuracy of the developed system. The 3-D
GIS map was generated on the basis of a global coordinate that was transformed
from a laser scanner coordinate using the position and posture data of a helicopter.
Xiang and Tian (2006) developed an agricultural remote sensing system using
an autonomous unmanned helicopter to acquire the crop field image at the right
time and right place with high image resolution. The autonomous unmanned heli-
copter-based agricultural remote sensing system has a GPS, inertial measurement
units (IMU), and a geomagnetic sensor to detect the position, attitude, and velocity
of the helicopter. An autonomous controller was applied to guide the helicopter to
arrive at desired positions. A multispectral camera mounted on the helicopter had a
pan/tilt platform to adjust the camera posture according to the altitude of the heli-
copter for avoiding image distortion. With the help of a ground station computer, the
helicopter maintained communication in real time to monitor flight parameters and
send out control command. This system can capture multispectral images available
for multipurpose agricultural RS research. Sullivan et al. (2007) investigated the use
of a UAV equipped with a TIR sensor as a less expensive system for detecting cot-
ton (
Gossypium hirsutum
L.) response to irrigation and crop residue management.
Aside from image acquisition, ground truth data were measured within a 1-m radius
of each sample location that consisted of soil water content (0-25 cm), stomatal con-
ductance, and canopy cover. An RTK-GPS survey unit was used for georeferencing
of all sample locations. The stability and repeatability of the UAV system during
an acquisition were assessed according to the analysis of sample locations acquired
in multiple flight lines. The results show that a coefficient of variation (CV) < 40%
was exhibited by approximately 70% of sample locations present in multiple flight
lines. Moreover, the differential observed by the UAV between relative differences
in canopy response to irrigation and crop residue cover management was more accu-
rate compared to that of ground measurements of stomatal conductance, which were
labor and time intensive. Within-season canopy stress can be well managed by using
TIR imagery acquired with a low-altitude UAV. Sullivan et al. (2007) assessed the
use of UAV imagery for quantitative monitoring of wheat crop in small plots. They
acquired multiple views in four spectral bands corresponding to blue, green, red, and
NIR to monitor 10 varieties of wheat grown in trial microplots in southwest France.
A robust and stable generic relationship was established on one hand, leaf area index
and NDVI and, on the other hand, nitrogen uptake and green NDVI (GNDVI). On
the basis of using a validation protocol, a precision level of 15% was obtained in the
biophysical parameters estimation while using these relationships.
Huang et al. (2008) developed a low-volume spray system installed on a fully
autonomous, unmanned vertical takeoff and landing helicopter to apply crop protec-
tion products on specified crop areas. Details were discussed on the development of
the spray system and its integration with the flight control system of this helicopter.
Monitored by GPS, the preset positional coordinates were used to trigger sprayer
actuation. The results show a potential of using the developed spray system coupled
