Agriculture Reference
In-Depth Information
effectiveness. There are certain advantages in acquiring high-resolution images
using Unmanned Aerial Vehicles (UAVs) over piloted aircraft missions, including
lower cost, flexibility in mission planning, improved safety, and closer proximity to
the target. Moreover, UAVs can be applied in the areas that are not easily accessible
by personnel or equipment. The ground station is usually the one to control the UAV
navigation system and navigate it to reach the predefined waypoints (Xiang, 2006).
There are several terms used to describe a UAV, including robotic aircraft, pilotless
airplane, remotely piloted vehicle, automatically piloted vehicle, drone, unmanned
aircraft, and remotely operated aircraft (Newcome, 2004). The application of UAVs
has many advantages such as ease, rapidity, and cost of flexibility of deployment that
makes UAVs available in many land surface measurement and monitoring applica-
tions. Since the introduction of the first unmanned remote-controlled aircraft in 1916,
UAV applications have been dominated by the military; however, civilian science
applications have recently received more emphasis, including UAVs for agriculture.
UAV-based agricultural remote sensing system can provide good flexibility in crop
image collection (Ahamed et al., 2010). Small, low-altitude UAV platforms offer
opportunities for monitoring of crops, coastal algal blooms, riparian and rangeland
vegetation. and even for photogrammetric and laser scanning, whereas high-altitude
UAV systems are ideal for innovative atmospheric science (primarily).
In the past decade, a steady flow of high-quality peer-reviewed papers and
research theses have been published on remote sensing from UAV platforms for
innovative applications. Numerous papers have been published about UAV systems
in the fields of nitrogen status of crops (Hunt et al., 2005), thermal and multispec-
tral sensors for estimating water stress in fruit crops (Berni et al., 2009), mapping
of knapweed in Utah rangelands (Hardin and Jackson, 2005), forestry and agricul-
ture (Grenzdoerffer et al., 2008), and rangeland vegetation (Rango et al., 2006).
GopalaPillai and Tian (1999) investigated the use of high-resolution color infrared
images acquired with an airborne digital camera to detect infield spatial variability
in soil type and crop nutrient stress, and to analyze spatial variability in yield. Spatial
yield models obtained 76-98% of yield variation on uncalibrated reflectance bands
of image in each field, whereas an accuracy of 55-91% of yield was obtained on
the basis of a linear regression model in different fields and seasons. Sugiura et al.
(2002) developed a system that can use the imaging sensor mounted on an unmanned
helicopter to generate a map regarding crop status. The unmanned helicopter has a
real-time kinematic (RTK)-GPS adopted as positioning sensor and an inertial sensor
to provide posture (i.e., roll and pitch angles). Moreover, the helicopter is equipped
with a geomagnetic direction sensor (GDS) to produce an absolute direction. As a
result, the image taken by the helicopter generated a map including 41 cm error. To
achieve uniform crop growth within a potato field, Yokobori et al. (2004) drew maps
of surface humus content growth, yield, and starch value using images obtained from
an unmanned helicopter in a test field whose preceding crops were carrot and soy-
bean. Iwahori et al. (2004) generated a 3-D GIS map of a farm field using the survey
system developed based on an unmanned helicopter that was mounted with an RTK-
GPS as a positioning sensor and an inertial sensor to provide posture (roll and pitch
angles). The helicopter was also equipped with a GDS to output absolute direction
and a laser scanner adopted to detect the distances between a helicopter and ground.
