In any experimental farm, categorized farm plots and
crop-wise sub plots will be present. The SDSS gives a
bird’s-eye view of the experimental farm layout (Figure
6). From this layout, the user can choose to view particular
crop fields and data for analysis and using spatial
query, the user can obtain plot-wise crop cultivation information.
Figure 7 a and b provides details of complete
experimental plot layout along with selected crop layout
respectively. Therefore, the spatial query helps to locate
the plots in which paddy is being grown. For example,
suppose the user wants to know the availability of vacant
plots in a particular month for planning future experiments,
based on the same spatial query, the user will get a
graphical representation report about vacant plots. This
spatial query for vacant plots will be executed based on
sowing date + crop maturity period in days (which is usually
less than the system date). Here, the system will calculate
the harvest date based on crop maturity days and
the user can frame queries like which plots are to be sown
during a particular month, and plot-wise crop variety
information (Figure 8). So based on individual researcher
requests, viz. plot size, soil type, irrigation facility, etc.
farm managers can allot plots for their experiments from
the available plots and can also pre-plan to distribute the
available resources, viz. labour, machines, fertilizers,
irrigation, etc. This information also helps the researchers
further, to be prepared in advance for their experiments.
Map-linked data tables can be accessed by pointing to a
specific location on the farm map and any tabular data
associated with the specific geographic location in the
farm can be viewed and accessed for additional analysis
through web interface (Figure 9).
In any experimental farm, categorized farm plots and
crop-wise sub plots will be present. The SDSS gives a
bird’s-eye view of the experimental farm layout (Figure
6). From this layout, the user can choose to view particular
crop fields and data for analysis and using spatial
query, the user can obtain plot-wise crop cultivation information.
Figure 7 a and b provides details of complete
experimental plot layout along with selected crop layout
respectively. Therefore, the spatial query helps to locate
the plots in which paddy is being grown. For example,
suppose the user wants to know the availability of vacant
plots in a particular month for planning future experiments,
based on the same spatial query, the user will get a
graphical representation report about vacant plots. This
spatial query for vacant plots will be executed based on
sowing date + crop maturity period in days (which is usually
less than the system date). Here, the system will calculate
the harvest date based on crop maturity days and
the user can frame queries like which plots are to be sown
during a particular month, and plot-wise crop variety
information (Figure 8). So based on individual researcher
requests, viz. plot size, soil type, irrigation facility, etc.
farm managers can allot plots for their experiments from
the available plots and can also pre-plan to distribute the
available resources, viz. labour, machines, fertilizers,
irrigation, etc. This information also helps the researchers
further, to be prepared in advance for their experiments.
Map-linked data tables can be accessed by pointing to a
specific location on the farm map and any tabular data
associated with the specific geographic location in the
farm can be viewed and accessed for additional analysis
through web interface (Figure 9).
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