Extensive
research
and
modern
advancements
have
proven
that
Artificial
Intelligence
(AI)
has
brought
a
drastic
change
for
all
the
sectors
of
economy
including
rural
agricultural
sector.
This
paper
is
focused
at
applying
method
Deep
Learning
(DL)
rice
leaf
disease
identification
-
major
problem
in
farming
industry.
project
incorporates
methods
like
data
preprocessing,
model
training,
evaluation
with
help
deep
learning
architectures
Tensorflow,
transfer
learning,
Conv2D
BatchNormalization.
Thus,
combination
these
intelligent
techniques,
contribution
this
proposed
custom
acquired
an
accuracy
95%
while
using
noticeably
lesser
number
layers
when
compared
to
some
popular
pre-trained
AI
models
MobileNet,
ResNet
InceptionV3.Deep
immense
potential
demonstrate
benefits
agriculture.
There
are
countless
opportunities
detect
diseases
early
objective
improve
crop
management
productivity
through
(AI).
Keeping
mind,
training
been
optimized
ImageDataGenerator
followed
by
strategic
callbacks.
The
result
speaks
itself
as
it
provides
precise
identify
disease.
Additionally,
talks
about
precision
agriculture
its
importance
It
highlights
prospective
increase
efficiency,
reduce
resource
wastage,
foster
sustainability
Through
research,
evident
utilization
sector
can
facilitate
informed
decision-making,
optimization,
detection
plant
diseases.
Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 24 - 42
Опубликована: Июль 12, 2024
The
soil
microbiome
in
particular
is
essential
for
preserving
plant
health
and
biomass
production.
management
of
microbial
communities,
whether
targeted
or
inadvertently,
appears
to
have
potential
improving
food
crop
yield,
quality,
sustainability
the
long
run.
With
development
innovative
nano-tools
quick
disease
diagnosis
improved
nutrient
uptake,
nanotechnology
holds
promise
advancing
agricultural
industries.
Utilizing
nano-materials
agriculture
offers
a
special
chance
maintain
increase
yield.
use
great
specific
applications
such
as
nano-pesticides
fertilizers
that
can
boost
productivity
without
contaminating
soils
offer
protection
against
diseases
insect
pests.
This
chapter
provides
current
updates
along
with
issues,
climate
change
security
also
future
prospects
The
world’s
increasing
population
has
a
higher
demand
for
food
and
suitable
environment.
However,
using
conventional
farming
methods
industrial
agrochemicals
leads
to
environmental
risk,
which
is
significant
threat
the
next
generation.
So,
nanotechnology
can
be
blessing
saving
our
environment
producing
risk-free
foods
at
minimal
cost
in
an
eco-friendly
way.
Nanoparticles
(NPs)
used
as
nanopesticides,
nanofertilizers,
nanosensors,
nanopriming
agents,
other
applications
agriculture
help
mitigate
issues
such
high
production
costs,
excessive
pesticide
fertilizer
requirements,
soil
depletion,
various
biotic
abiotic
challenges.
A
variety
of
important
information
from
different
research
findings
on
metal
nanoparticles,
their
characteristics,
synthesis
process,
roles
precision
sustainable
are
included
this
article.
This
literature
review
discusses
benefits
nanoparticles
plant
growth
development,
ease
green
nanoparticle
over
chemical
physical
approaches,
effects
agriculture.
Future
perspectives
also
covered
article
based
these
impacts.
Metal
biosensors
seed-priming
materials,
contribute
seed
germination
even
adverse
conditions.
overall,
potentiality
lieu
inorganic
possible
contribution
Agronomy,
Год журнала:
2024,
Номер
14(9), С. 2175 - 2175
Опубликована: Сен. 23, 2024
Agriculture
plays
a
fundamental
role
in
ensuring
global
food
security,
yet
plant
diseases
remain
significant
threat
to
crop
production.
Traditional
methods
manage
have
been
extensively
used,
but
they
face
drawbacks,
such
as
environmental
pollution,
health
risks
and
pathogen
resistance.
Similarly,
biopesticides
are
eco-friendly,
limited
by
their
specificity
stability
issues.
This
has
led
the
exploration
of
novel
biotechnological
approaches,
development
synthetic
proteins,
which
aim
mitigate
these
drawbacks
offering
more
targeted
sustainable
solutions.
recent
advances
genome
editing
techniques—such
meganucleases
(MegNs),
zinc
finger
nucleases
(ZFNs),
transcription
activator-like
effector
(TALENs)
clustered
regularly
interspaced
short
palindromic
repeats
(CRISPR)—are
precise
approaches
disease
management,
technical
challenges
regulatory
concerns.
In
this
realm,
nanotechnology
emerged
promising
frontier
that
offers
solutions
for
management.
review
examines
nanoparticles
(NPs),
including
organic
NPs,
inorganic
polymeric
NPs
carbon
enhancing
resistance
improving
pesticide
delivery,
gives
an
overview
current
state
managing
diseases,
its
advantages,
practical
applications
obstacles
must
be
overcome
fully
harness
potential.
By
understanding
aspects,
we
can
better
appreciate
transformative
impact
on
modern
agriculture
develop
effective
strategies
enhanced
agricultural
productivity.
Notulae Scientia Biologicae,
Год журнала:
2024,
Номер
16(4), С. 12175 - 12175
Опубликована: Дек. 17, 2024
In
melon
(Cucumis
melo
L.)
cultivation,
there
is
very
little
evidence
about
the
improvement
of
plants
in
face
biotic
and
abiotic
factors,
photosynthetic
metabolisms
crop
productivity
through
fertilization
addition
cobalt.
The
objective
our
research
was
to
demonstrate
effect
CO3
O4
NP's
on
growth,
yield,
fruit
weight,
TSS,
firmness,
cobalt
content
bioactive
compounds
fruits
established
open
field.
For
this,
a
randomized
complete
block
design
implemented
with
five
treatments
control
(0,
5,
10,
15,
20
25
mg
L
-1
CO
3
O
4
NP's)
three
replicates
respectively.
use
at
dose
increased
yield
by
40%
(42-ton
ha
-1),
compared
which
had
30
50-ton
-1.
As
well
as
an
increase
weight
highest
doses
9%
control.
On
other
hand,
were
no
significant
differences
concentration
pulp
peel.
Bioactive
up
2%
firmness
soluble
solids
not
significantly
affected.
Results
indicated
that
NP's,
provides
higher
antioxidant
capacity
(anthocyanianins)
show
better
performance
under
these
experimental
conditions.
therefore,
are
viable
option
for
improving
physicochemical
properties
fruit.
Extensive
research
and
modern
advancements
have
proven
that
Artificial
Intelligence
(AI)
has
brought
a
drastic
change
for
all
the
sectors
of
economy
including
rural
agricultural
sector.
This
paper
is
focused
at
applying
method
Deep
Learning
(DL)
rice
leaf
disease
identification
-
major
problem
in
farming
industry.
project
incorporates
methods
like
data
preprocessing,
model
training,
evaluation
with
help
deep
learning
architectures
Tensorflow,
transfer
learning,
Conv2D
BatchNormalization.
Thus,
combination
these
intelligent
techniques,
contribution
this
proposed
custom
acquired
an
accuracy
95%
while
using
noticeably
lesser
number
layers
when
compared
to
some
popular
pre-trained
AI
models
MobileNet,
ResNet
InceptionV3.Deep
immense
potential
demonstrate
benefits
agriculture.
There
are
countless
opportunities
detect
diseases
early
objective
improve
crop
management
productivity
through
(AI).
Keeping
mind,
training
been
optimized
ImageDataGenerator
followed
by
strategic
callbacks.
The
result
speaks
itself
as
it
provides
precise
identify
disease.
Additionally,
talks
about
precision
agriculture
its
importance
It
highlights
prospective
increase
efficiency,
reduce
resource
wastage,
foster
sustainability
Through
research,
evident
utilization
sector
can
facilitate
informed
decision-making,
optimization,
detection
plant
diseases.
