With
the
continuous
development
of
China's
Internet
Things
technology,
application
this
technology
in
agriculture
is
becoming
more
and
extensive.
agricultural
irrigation
has
shown
a
trend
automation,
but
current
automatic
system
cannot
automatically
control
amount
water
irrigation,
it
not
intelligent
enough.
The
closely
related
to
modern
green
far
from
meeting
requirements
China
's
agriculture.
When
too
large,
will
cause
serious
waste
resources,
when
small,
affect
growth
crops.
Based
on
situation,
paper
designs
an
system,
which
combines
communication
module
with
single-chip
microcomputer,
monitors
data
crop
environment
real
time
help
ZigBee
wireless
sensors.
According
different
models
soil
nutrient
content
characteristics
laws
fertilizers
needed
by
crops,
platform
model
for
real-time
monitoring
information
based
constructed.
After
testing,
can
predict
fertilizer
required
process
growth,
so
as
make
timely
appropriate
decisions
scientifically,
improve
level
management.
greatly
alleviate
problem
resources
compaction.
It
only
realize
saving
energy
saving,
also
promote
production
income
increase.
provides
technical
basis
support
follow-up
Indian Journal of Science and Technology,
Journal Year:
2023,
Volume and Issue:
16(37), P. 3090 - 3099
Published: Oct. 9, 2023
Objective:
The
goal
of
the
proposed
work
is
to
create
a
smart
farming
methodology
that
automates
crop
suggestions,
irrigation,
disease
management
using
machine
learning,
and
pest
utilising
Internet
Things
concepts.
Methods:
approach
implements
in
four
different
phases.
Crop
selection
recommended
based
on
suitability
soil
XGBoost
learning
algorithm
Kaggle
Recommendation
dataset.
Smart
irrigation
has
been
implemented
LM35
temperature
sensor
DHT22
humidity
sensor.
Convolutional
neural
network
models
were
used
for
automatic
detection.
An
IoT-based
system
management.
Findings:
This
uses
hybrid
strategies
increase
agricultural
productivity
best
possible
circumstances.
Ten
fields
cultivate
rice
vegetables
like
tomatoes,
lady
fingers,
brinjal
plants
Southern
parts
Tamil
Nadu
have
as
case
studies
research.
type
resulted
an
yield
62%
tomato
crops,
71%
77%
ladies
finger.
helped
reducing
consumption
water
by
34.38%
rice,
56.17%
brinjal,
60%
finger
64.45%
tomatoes.
Tomato
leaf
diseases
could
be
automatically
identified
with
accuracy
96.24%.
Novelty:
choose
crops
first
time
98.62%.
sensors
pH
meter.
model
improved
transfer
techniques
hyperparameter
tuning
achieve
Keywords:
Farming;
Neural
Networks;
Extreme
Gradient
Boosting;
Deep
Learning
Rice
cultivation,
a
staple
crop
that
sustains
billions
of
people
globally,
faces
significant
challenges
in
terms
water
management,
labor
efficiency,
and
productivity.
China,
as
the
largest
producer
consumer
rice,
predominantly
relies
on
traditional
flood
irrigation
via
open
canals,
which
is
both
labor-intensive
results
substantial
wastage.
To
address
these
challenges,
this
research
presents
development
implementation
an
intelligent
system
tailored
to
canal
rice
cultivation.
This
leverages
automation
remote
monitoring
enhance
convenience,
productivity,
safety,
especially
for
aging
farming
demographic.
The
system's
core
components
include
lifting
stations,
gate
transformations,
wireless
control,
management
platform.
By
seamlessly
integrating
elements,
optimizes
distribution,
reduces
requirements,
enhances
yields.
contributes
modernization
digitization
cultivation
practices,
emphasizing
resource
conservation
addressing
agricultural
sector
challenges.
Through
multi-site
trials,
capabilities
are
demonstrated
significantly
reduce
consumption
paddies
while
maintaining
Water
savings
range
from
2.9%
19.3%,
depending
seasonal
conditions.
Furthermore,
control
features
needs
by
approximately
35-40%,
offering
convenience
productivity
benefits.
work
highlights
potential
improve
sustainability
farming,
providing
pathway
towards
efficient
application,
savings,
increased
With
the
continuous
development
of
China's
Internet
Things
technology,
application
this
technology
in
agriculture
is
becoming
more
and
extensive.
agricultural
irrigation
has
shown
a
trend
automation,
but
current
automatic
system
cannot
automatically
control
amount
water
irrigation,
it
not
intelligent
enough.
The
closely
related
to
modern
green
far
from
meeting
requirements
China
's
agriculture.
When
too
large,
will
cause
serious
waste
resources,
when
small,
affect
growth
crops.
Based
on
situation,
paper
designs
an
system,
which
combines
communication
module
with
single-chip
microcomputer,
monitors
data
crop
environment
real
time
help
ZigBee
wireless
sensors.
According
different
models
soil
nutrient
content
characteristics
laws
fertilizers
needed
by
crops,
platform
model
for
real-time
monitoring
information
based
constructed.
After
testing,
can
predict
fertilizer
required
process
growth,
so
as
make
timely
appropriate
decisions
scientifically,
improve
level
management.
greatly
alleviate
problem
resources
compaction.
It
only
realize
saving
energy
saving,
also
promote
production
income
increase.
provides
technical
basis
support
follow-up
