Agriculture Reference
In-Depth Information
broadcast by a set of navigation satellites, and therefore can provide continuous position
information while the equipment is in motion. The irreplaceable value of GPS sensing
systems are its capability to provide precise location information at any time. This allows
spatially variable field operation data to be measured and mapped (Stafford and Ambler,
1994). Numerous applications of GPS positioning in support of precise and/or automated
agricultural production have been developed since the 1990s. A few influential examples
are the use of the GPS to record yield variation over a field on combines during harvest
(Auernhammer et al., 1994), to apply a variable rate of fertilizer and pesticide in terms of
a predetermined application map (Schueller and Wang, 1994), and to navigate a tractor
and implement along a predetermined path with 1-2 cm level relative precision, when
used in a high-precision differential mode (Larsen et al., 1994).
The special sensors often refer to soil, field, crop, and yield sensors that require
additional development to make them usable in AIS. One example is the image-
based crop nutrition stress sensor. The prototype sensor was developed based on a
general purpose multispectral camera, and from it the raw data acquired from the
field is merely a multispectral image of the crops and requires having an image pro-
cessing procedure to extract crop nutrition stress information from the obtained raw
image (Kim et al., 2000b). Another example is the use of a light detection and rang-
ing (LiDAR) sensor to estimate crop density, which obtains a cloud of laser scan-
ning points, and also requires extracting bulk parameters from the obtained cloud
to reconstruct a measurable “field view” for supporting the estimation (Saeys et al.,
2009). Although many of the special sensors are still in concept development and
approval phases, commercial products are also available and are being adopted by
agricultural users. A couple of examples of commercially available crop/soil sensors
are handheld chlorophyll meters, and various kinds of soil sensors, both of which
could provide the needed crop and soil data to support an effective AIS.
Remote sensing is another type of special sensing system for precision farming,
which collects production data from a distance, as far as from a satellite and as close
as from a handheld instrument, and capable of revealing in-season variability of
crop growth condition for supporting timely management decisions to gain optimal
yield of the current crop (Brisco et al., 1998; Jones et al., 2007). Knowing the spatial
variability of production data, such as soil properties and moisture, crop yield, crop
canopy stress and biomass volume, crop growth and pest conditions, within a field is
very important for practicing precision farming. Remote sensing provides an effec-
tive way to obtain a precise picture of the spatial variability of related parameters
within a field (Lee et al., 2010). Although diverse types of sensors, such as satel-
lite imagery, airborne multispectral or hyperspectral imaging, and thermal imag-
ing, are used in remote sensing systems, many of them are based on the same basic
technology—the imaging technology, and therefore carry similar features: either
obtaining a broad view of an entire field scene under a price of limited resolution
or capturing the high-resolution details of a small area. Image panoramic technol-
ogy could seamlessly integrate ground-based multispectral images to combine the
advantages of both satellite-based and ground-based sensing, and result in imagery
that could present both the broad view of a field scene and detailed observation of
crop growth condition, to offer a very informative means for detecting crop produc-
tion information (Kise and Zhang, 2008).
