Agriculture Reference
In-Depth Information
3.4.5 D
ATA
V
ISUALIZATION
Data visualization, a study of the visual representation of data and supported mainly
by data transformation, system modeling, computer graphics, and human-computer
interaction technology, is one of the most influential technologies for data users.
In terms of their nature, the data could be visualized in different ways. Figure 3.2
presents two examples of data visualization: panel (a) is a yield variation distribution
within a field and panel (b) is the desired pathway for guiding a tractor traveling in
between crop rows. As a new technique of data analysis and processing, data visu-
alization has an increasing importance in exploring and analyzing large amounts of
multidimensional information useful in agricultural automation. The fundamental
goal of data visualization technology is to find the implicit information from the
large amounts of data to provide valuable assistance for decision-making. Mapping
data in a visually observable manner could help data users comprehensively under-
stand the spatial information and allow them to use the information for better pro-
duction management.
One reported example of data visualization was the use of a visualization mod-
ule for the exploration, description, and analysis of spatial and temporal patterns in
a Modeling Applications System Integrative Framework developed by Gage et al.
(2001). Visual presentation was claimed to be preferable for comprehending informa-
tion contained in large datasets associated with models that simulate processes and
patterns at regional scales. Another example was a model-based visual growth sys-
tem developed for managing rice production (Liu et al., 2009). This visualized pro-
duction management system integrated a growth simulation model with the weather,
soil, variety, and cultivation techniques databases, and is capable of presenting rel-
evant data in either two-dimensional (2-D) or 3-D visualization format. This sys-
tem could predict growth progress and visualize morphological architecture of rice
plants under various environments, genotypes, and management strategies. In many
applications, the visualization of data should be able to graph the responses against
certain factors and/or conditioning on other factors. Fuentes et al. (2011) proposed
(a)
(b)
FIGURE 3.2
Example of data visualization: (a) grain yield variation in a field; and (b) desired
pathway for guiding a tractor traveling in between crop rows.
