Agriculture Reference
In-Depth Information
management zones, which are obtained by classifying image pixels into categories
of similar spectral response, should reduce both the variance within each zone and
the number of soil samples required to characterize each zone. Yang and Anderson
(1999) used airborne multispectral imagery and unsupervised classification tech-
niques to determine within-field management zones for two grain sorghum fields
with multiple stresses. Two of the zones identified were soil related. One represented
areas with insufficient soil moisture and the second depicted areas where plants
suffered severe chlorosis due to iron deficiency. The remaining zones represented
areas with different production levels due to a combination of soil and environmental
factors.
Airborne images taken at early stages of crop growth can reveal plant growth
patterns that could be observed in images acquired later in the season. However,
imagery obtained at the time or shortly after the crop has reached its maximum
canopy cover shows more stable patterns that remain during the rest of the growing
season. The important implications of these observations are that plant growth varia-
tions and stress conditions can be detected within the growing season so that proper
measures may be taken to correct some of the problems such as nutrient deficien-
cies. Significant correlations existed between yield and image data, and yield was
more strongly related to images taken around peak growth, indicating that imagery
taken at this particular stage could be a better indicator of yield (Yang and Everitt,
2002). Significant differences in grain yield among the spectrally determined zones
indicate that aerial digital imagery can adequately capture within-field yield vari-
ability. Although the spatial plant growth patterns identified by within-season digital
images may not always perfectly match those revealed on yield maps from yield
monitor data, aerial digital imagery does provide important information for both
within-season and after-season management in precision agriculture.
Data from yield monitors have also been investigated as a means of generating
management zones. Spatial and temporal yield patterns can be variable and incon-
sistent between growing seasons (Colvin et al., 1997; Stafford et al., 1998). Although
yield monitor data alone might be unsuitable for the delineation of management
zones, they are a valuable source of ancillary information, especially when compiled
over several growing seasons (Stafford et al., 1998). Generally, the techniques for
delineating management zones involves the use of multiple sources of data, includ-
ing yield monitor data, soil properties, remotely sensed imagery, and topography
(Yang et al., 1998; Fleming et al., 2004; Hornung et al., 2006; Khosla et al., 2008;
Franzen et al., 2011).
4.3.3 S
ITE
-S
PECIFIC
A
PPLICATION
M
APS
Although identifying spatial variability of soil and crop growth with fields is the first
important step toward site-specific management, using that variability to formulate
variable rate application plans of farming inputs is another essential step in precision
agriculture. The major types of crop production inputs for variable rate application
include fertilizers, limestone, pesticides, and seeds. There are two basic methods
for implementing variable rate application: map-based and sensor-based. Map-based
variable rate application systems adjust the application rate of a product based on
