Agriculture Reference
In-Depth Information
4.3 DATA ANALYSIS AND MANAGEMENT
Data analysis and management is probably the most difficult area and the area where
the greatest amount of work has been done in precision agriculture. It involves
manipulating and analyzing measured data and determining proper control actions
or at least presenting useful information to the farmer. One of the most important
aspects of these data is their spatial or geographic nature. Therefore, spatial infor-
mation technologies such as GIS and geostatistics are important for processing this
type of data and for developing management zones or site-specific application maps.
4.3.1 GIS
By the U.S. Geological Survey (2011), a GIS is defined as “a computer system capa-
ble of capturing, storing, analyzing, and displaying geographically referenced infor-
mation; that is, data identified according to location. Practitioners also regard the
total GIS as including the procedures, operating personnel, and spatial data that go
into the system.” GIS has found many applications in precision agriculture as well
as in other areas.
There are two different types of GIS data: raster and vector. Raster data are a
cell-based data format and each cell has a value. Images and grids are examples of
raster data. Vector data are based on coordinates of different map features. A point
is stored as a single
x, y
coordinate, and a line is stored as a pair of
x, y
coordinates.
Similarly, a polygon is stored as a set of
x, y
coordinates.
Precision farming data often need to be interpolated to fill in gaps between data
points. Common interpolation methods include nearest neighbor, local averaging,
inverse distance weighting (IDW), contouring, and kriging. For the nearest neighbor
method, an unknown point is set equal to its nearest neighbor. Local averaging is to
estimate unknown values by a simple average of a selected number of points around
the desired location. IDW is based on the fact that points closer to an unknown point
are more likely to have similar properties than those farther away, and thus weights
are determined inversely proportional to the distance between data points when
estimating the unknown point. Contouring is to connect points of the same value,
and unknown value can be estimated between the known points. Kriging is known
to be an optimal interpolation method. It first estimates the variability of the known
data set. Then to estimate unknown value, IDW is conducted for points closer, and
equal weights are used for points farther away. This method is slower than others
because of intensive computations.
Like GPS, GIS can be considered a major tool for implementing precision agri-
culture. In this regard, Earl et al. (2000) provided an overview of the role of GIS in
autonomous field operations, emphasizing that GIS could play an important role in
simultaneously interpreting multiple spatial and temporal field attributes for efficient
farm management. Pierce and Clay (2007) also described various GIS applications
in agriculture including nitrogen management of sugar beet using GIS and remote
sensing, development of productivity zones from multiple years of yield data, site-
specific weed management, soil salinity mapping, variable depth tillage assessment
using GIS, and on-the-go soil strength sensing. However, Nemenyi et al. (2003)
