Agriculture Reference
In-Depth Information
One of the major challenges of increasing importance in precise or automated
agricultural production is how to effectively utilize the collected data from various
sources to support a profitable field operation. As such data are often stored in a GIS
(geographic information systems) format, it is essential for AIS to be able to collect
useful data from GIS data files, as well as to record collected data in GIS formatted
files. The latter is especially important for production traceability, as it could record
all relevant data with georeference information in a time sequential way. Efforts have
made such technologies applicable in the field, and one of such examples has been
the use of an RFID (radio-frequency identification) and barcode registration system
to automatically match bins containing harvested fruit with corresponding trees dur-
ing harvest in orchards (Ampatzidis and Vougioukas, 2009). There are successful
applications of using georeference data in supporting automated field operations. A
few representative examples include electronically recording field operation data in
terms of the position and time of acquisition to provide a basis for managing autono-
mous field operations (Earl et al., 2000), retrieving soil profiles from georeferenced
soil databases as the input parameters in simulating crop growth, development, and
yield for support to make optimal site-specific management decisions (Gijsman
et al., 2007); mapping tractor-implement performance with its geographical loca-
tion (Yahya et al., 2009); and integrating a large data flow acquired on agricultural
machines during field work with web-based services, and for supporting informa-
tion-steered agricultural production (Steinberger et al., 2009).
3.3.3 D
ATA
C
OMPUTING
E
LEMENTS
Data computing elements are the brain of AIS. Because of the particular applications
in agriculture, the most common data computing elements used for agricultural data
acquisition and management today are personal computers (PCs) with Windows
®
operating systems. A typical computerized data acquisition system can be connected
to more than 300 sensors, and take more than 10 million samples per second. When
more sensors are needed or higher sampling rate is required, some higher perfor-
mance computers, such as workstations and servers, can often be used. These com-
puters are often operated using UNIX operating systems. In many on-equipment
data computing applications, from field data acquisition to variable rate fertilizing
control, some types of embedded computers are commonly used.
A few early examples of using computers in agriculture included a data management
system built on a Sperry 1100 mainframe computer for collecting, formatting, storing,
and retrieving data on crop growth and development, pesticide applications, and envi-
ronmental conditions over a wide variety of crop types (Muller and Harriott, 1984); and
the use of a distributed network of computers for monitoring and controlling the environ-
ment of research greenhouses, in which a central computer networked three microcom-
puters to control air temperature and nutrient supply rate in nine management zones by
providing setpoints to each microcomputer (Hooper, 1988). The use of a portable laptop
computer in field data acquisition was reported as early as the middle 1980s, for a real-
time rain gauge data reading (Williams and Erdman, 1987).
Although the use of computers in the 1980s was mostly as information manage-
ment tools, the progress in the 1990s prepared computers more as an integrated
