Agriculture Reference
In-Depth Information
to machinery operational data, crop growth information, and field geographical data,
to optimize the production process.
Traditionally, agricultural production relied on very little operation relevant data
other than some vague climate information such as the planting season. Farmers plan-
ning their operations mainly made decisions based on their experience. No sensor was
used on farm equipment in the early days other than the farmer. As modernization and
mechanization of agriculture progressed, more efficient agricultural production required
a better knowledge of the situation. As a result, data became more and more important
in operation planning, and some on-machinery instruments, such as odometer and fuel
gauge, were added to tractors in the 1920s (Leffingwell, 2005). Those data could only
provide individual pieces of information, often unrelated, and required human work-
ers to interpret their influences on production, which required some special skill and
extensive experience to do so. It became an obstacle for farmers to achieve efficient
production, especially when the scale and/or the complexity of the operation increased.
With the emergence of precision farming technologies in modern crop production, the
optimization of site-specific crop production management within a field requires soil
fertility and crop growth information, with respect to specific regions within the field
(Barnes et al., 2003). To obtain such information, various sensing devices, such as soil
sensors, crop growth condition sensors, positioning sensors, and remote sensing sys-
tems, have been used in precision farming operations since the 1990s (Hamit, 1996;
Wehrhan and Selige, 1997; Kim et al., 2000b; Weiss and Baret, 2000; Basnyat et al.,
2004; Lee et al., 2010). All these sensing devices, along with many machinery status
sensors, collectively serve as the data collecting elements in AIS.
In terms of their usage, data collecting elements in AIS can be classified into
several categories: general purpose sensors, special sensors, and databases. General
purpose sensors are often used to provide users with some well-defined physical
data. Some examples are temperature, displacement, force or pressure, moisture or
humidity, resistance or capacitance, flow rate, and position. Among them, tempera-
ture is an important parameter for both machinery systems and crop systems. A
wide selection of temperature sensors could be applied to AIS applications. For an
example, there is always a thermometer in tractors to monitor engine temperature
as an indirect indication of engine operating conditions. It is also common to detect
canopy temperature as an indicator of crop water stress severity (Gonzalez-Dugo et
al., 2006) and to measure soil temperature for monitoring soil moisture, or agricul-
tural drought in precision farming practices (Cicek et al., 2010; Champagne et al.,
2011). Displacement measurement is another common parameter being measured.
Other examples are sowing depth measurement (McGahan and Robotham, 1992),
plow depth control (Panigrahi et al., 1990), and automatic steer control (Wu et al.,
2001). In addition, displacement is often an indirect measurement of velocity, accel-
eration, and strain, as well as (by the use of elastic elements) force and pressure.
Other commonly measured physical parameters in agricultural practices include,
but are not limited to, speed, torque, pressure, flow rate, and moisture; and all have a
long list of commercial products to choose from for AIS applications.
Global Positioning System (GPS) is another general purpose sensing system, which
has found wide application in agriculture, especially in precision farming. A GPS receiver
calculates its position on the Earth in terms of the received real-time positioning signals
