Plant
tissue
growth
can
be
regulated
and
controlled
via
culture
media
composition.
A
number
of
different
laborious
and
time-consuming
approaches
have
been
used
to
attempt
development
of
optimized
media
for
a
wide
range
of
species
and
applications.
However,
plant
tissue
culture
is
a
very
complex
task,
and
the
identification
of
the
influences
of
process
factors
such
as
mineral
nutrients
or
plant
growth
regulators
on
a
wide
spectrum
of
growth
responses
cannot
always
well
comprehended.
This
study
employs
a
new
approach,
data
mining,
to
uncover
and
integrate
knowledge
hidden
in
mul
tiple
data
from
plant
tissue
culture
media
formulations
using
apricot
micropropagation
databases
as
an
example.
Neurofuzzy
logic
technology
made
it
possible
to
identify
relationships
among
several
factors
(cultivars,
mineral
nutrients
and
plant
growth
regulators)
and
growth
parameters
(shoots
number,
shoots
length
and
productivity),
extracting
biologically
useful
information
from
each
database
and
combining
them
to
create
a
model.
The
IF-THEN
rule
sets
generated
by
neurofuzzy
logic
were
completely
in
agree
ment
with
previous
findings
based
on
statistical
analysis,
but
advantageously
generated
understandable
and
Plant
tissue
growth
can
be
regulated
and
controlled
via
culture
media
composition.
A
number
of
different
laborious
and
time-consuming
approaches
have
been
used
to
attempt
development
of
optimized
media
for
a
wide
range
of
species
and
applications.
However,
plant
tissue
culture
is
a
very
complex
task,
and
the
identification
of
the
influences
of
process
factors
such
as
mineral
nutrients
or
plant
growth
regulators
on
a
wide
spectrum
of
growth
responses
cannot
always
well
comprehended.
This
study
employs
a
new
approach,
data
mining,
to
uncover
and
integrate
knowledge
hidden
in
mul
tiple
data
from
plant
tissue
culture
media
formulations
using
apricot
micropropagation
databases
as
an
example.
Neurofuzzy
logic
technology
made
it
possible
to
identify
relationships
among
several
factors
(cultivars,
mineral
nutrients
and
plant
growth
regulators)
and
growth
parameters
(shoots
number,
shoots
length
and
productivity),
extracting
biologically
useful
information
from
each
database
and
combining
them
to
create
a
model.
The
IF-THEN
rule
sets
generated
by
neurofuzzy
logic
were
completely
in
agree
ment
with
previous
findings
based
on
statistical
analysis,
but
advantageously
generated
understandable
and
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