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405
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I
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2252
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DO
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11591/i
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Evaluation Warning : The document was created with Spire.PDF for Python.
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2252
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d
iagn
o
s
i
s
pr
o
c
e
dur
e
i
s
de
l
i
be
r
a
t
e
to
c
a
r
r
y
o
ut
m
a
n
u
a
l
ly
a
n
d
a
n
o
t
h
e
r
r
e
a
l
i
t
y
t
h
a
t
t
h
e
o
pul
e
n
c
e
o
f
d
i
a
g
n
o
s
i
s
a
r
e
c
o
m
pa
r
a
bl
e
t
o
t
h
e
pa
t
h
o
l
o
g
i
s
t
’
s
pot
e
n
t
i
a
l
i
t
i
e
s
make
s
t
h
e
a
uto
m
a
t
i
c
d
i
a
g
n
o
s
i
s
i
s
s
ue
a
v
e
r
y
go
o
d
a
ppl
i
c
a
t
i
o
n
do
m
a
i
n
f
o
r
c
o
m
put
e
r
-
a
i
de
d
d
i
a
g
n
o
s
i
s
.
U
t
i
l
a
e
t
a
l.
[
3]
,
E
f
f
i
c
i
e
n
t
Ne
t
de
e
p
l
e
a
r
ni
ng
a
r
c
hi
t
e
c
t
ur
e
wa
s
pr
e
s
e
nt
e
d
i
n
p
l
a
n
t
l
e
a
f
il
l
ne
s
s
d
i
a
g
n
o
s
i
s
c
a
t
e
gor
i
z
a
t
i
o
n
s
ho
we
d
a
n
im
pr
o
v
e
d
a
c
c
ur
a
c
y
us
i
ng
tr
a
n
s
f
e
r
l
e
a
r
ni
ng.
B
e
s
t
a
r
t
i
f
i
c
i
a
l
ne
ur
a
l
n
e
t
wo
r
k
wa
s
i
de
n
t
i
f
i
e
d
i
ni
t
i
a
ll
y
a
n
d
t
h
e
n
w
i
t
h
t
h
e
o
b
t
a
i
n
e
d
s
h
a
pe
a
n
d
c
o
l
o
r
d
i
s
e
a
s
e
d
i
a
g
n
o
s
i
s
wa
s
m
a
de
i
n
[
4]
a
c
c
ur
a
t
e
l
y
.
R
i
c
e
i
s
c
u
l
t
i
va
t
e
d
g
l
o
b
a
ll
y
,
w
i
t
h
a
pa
r
t
i
c
u
l
a
r
e
m
p
h
a
s
i
s
o
n
As
i
a
n
c
o
un
t
r
i
e
s
,
wh
e
r
e
i
t
c
o
n
s
t
i
t
ut
e
s
a
s
i
g
ni
f
i
c
a
n
t
p
o
r
t
i
o
n
o
f
t
h
e
d
i
e
t
s
o
f
a
ppr
o
xi
m
a
t
e
l
y
h
a
l
f
o
f
t
h
e
wo
r
l
d
'
s
po
pu
l
a
t
i
o
n
.
I
n
s
p
i
t
e
o
f
t
hi
s
f
a
c
t
,
f
a
r
m
e
r
s
a
n
d
p
l
a
n
t
i
n
g
e
x
pe
r
t
s
s
ti
ll
c
o
m
e
a
c
r
o
s
s
wi
t
h
n
u
m
e
r
o
us
c
o
n
t
i
n
uo
us
h
ur
d
l
e
s
f
o
r
i
nn
u
m
e
r
a
bl
e
y
e
a
r
s
,
i
n
c
l
us
i
ve
o
f
r
i
c
e
d
i
s
e
a
s
e
s
.
R
a
t
h
o
r
e
e
t
al.
[
5]
f
o
c
us
wa
s
m
a
de
o
n
c
o
ns
t
r
uc
t
i
n
g
li
g
h
t
we
i
g
h
t
de
e
p
l
e
a
r
ni
ng
m
e
t
h
o
d
f
o
r
de
t
e
c
t
i
n
g
r
i
c
e
p
l
a
n
t
di
s
e
a
s
e
s
i
n
a
f
ur
t
h
e
r
pr
e
c
i
s
e
m
a
nn
e
r
,
t
h
e
r
e
f
o
r
e
m
i
n
im
i
z
i
ng
t
h
e
c
o
m
put
a
t
i
o
n
c
o
s
t
a
n
d
c
o
m
p
l
e
xi
t
y
.
A
s
ur
v
e
y
o
f
r
i
c
e
c
r
o
p
d
i
s
e
a
s
e
i
de
n
t
i
f
i
c
a
t
i
o
n
f
o
r
e
x
p
a
n
d
i
n
g
r
i
c
e
c
o
n
c
e
pt
i
o
n
wa
s
i
nve
s
t
i
ga
t
e
d
i
n
[
6]
.
D
i
f
f
e
r
e
n
t
p
l
a
n
t
d
i
s
e
a
s
e
h
a
s
a
c
r
uc
i
a
l
i
n
f
l
ue
n
c
e
o
n
f
o
o
d
c
r
o
p
y
i
e
l
d
a
n
d
upo
n
i
m
pr
o
pe
r
r
e
c
o
gn
i
t
i
o
n
wo
ul
d
s
pr
e
a
d
t
h
e
di
s
e
a
s
e
wi
de
ly
.
Ho
we
v
e
r
,
w
i
t
h
t
h
e
l
a
c
k
i
n
i
de
n
t
i
f
i
c
a
t
i
o
n
o
f
mi
n
ut
e
pl
a
n
t
l
e
s
i
o
n
f
e
a
t
ur
e
s
c
o
m
pr
o
m
i
s
e
d
t
h
e
pr
e
c
is
i
o
n
.
T
o
a
ddr
e
s
s
o
n
t
hi
s
i
s
s
ue
,
C
h
e
n
e
t
al
.
[
7]
,
de
e
p
l
e
a
r
ni
ng
t
e
c
hni
que
s
us
i
ng
e
n
s
e
m
bl
e
c
o
nv
o
l
ut
i
o
n
ne
t
wo
r
k
w
a
s
a
pp
l
i
e
d
w
i
t
h
t
h
e
pur
po
s
e
o
f
e
nha
n
c
i
ng
t
h
e
o
v
e
r
a
l
l
po
t
e
n
t
i
a
li
t
y
f
o
r
i
de
n
t
i
f
i
c
a
t
i
o
n
o
f
m
i
nut
e
p
l
a
nt
l
e
s
i
o
n
f
e
a
t
ur
e
s
.
H
o
we
v
e
r
,
e
r
r
o
r
r
a
t
e
wa
s
n
ot
f
o
c
us
e
d.
Va
ll
a
bh
a
j
o
s
y
u
l
a
e
t
al
.
[
8]
,
a
n
e
n
s
e
m
bl
e
n
e
ur
a
l
n
e
t
wor
k
b
a
s
e
d
o
n
t
h
e
t
r
a
n
s
f
e
r
l
e
a
r
ni
ng
m
e
c
h
a
ni
s
m
w
a
s
d
e
s
i
g
n
e
d
t
o
pl
a
n
t
l
e
a
f
d
i
s
e
a
s
e
de
t
e
c
t
i
o
n
.
He
r
e
w
i
t
h
a
l
o
s
s
f
u
nc
t
i
o
n
o
f
gr
a
d
i
e
n
t
s
e
r
r
o
r
f
a
c
t
o
r
wa
s
a
ddr
e
s
s
e
d,
t
h
e
r
e
f
o
r
e
e
n
s
ur
i
ng
e
a
r
l
y
d
i
s
e
a
s
e
de
t
e
c
t
i
o
n
.
Ye
t
a
n
ot
h
e
r
de
e
p
t
r
a
n
s
f
e
r
l
e
a
r
ni
ng
m
e
c
h
a
ni
s
m
f
o
r
i
n
t
e
l
li
ge
n
t
s
uppo
r
t
s
y
s
t
e
m
wa
s
de
s
i
g
n
e
d
i
n
[
9]
.
He
r
e
,
p
a
t
h
o
ge
n
da
m
a
ge
wa
s
e
ns
ur
e
d
i
n
a
n
a
c
c
ur
a
t
e
m
a
nn
e
r
.
A
a
ppr
a
i
s
a
l
o
f
s
o
phi
s
t
i
c
a
t
e
d
de
e
p
l
e
a
r
ni
ng
t
e
c
hni
qu
e
s
f
o
r
pl
a
n
t
di
s
e
a
s
e
r
e
c
o
gni
t
i
o
n
wa
s
i
nve
s
t
i
g
a
t
e
d
i
n
[
10]
.
M
o
t
i
v
a
t
e
d
by
t
h
e
a
b
o
v
e
i
s
s
ue
s
,
l
i
ke
,
pr
e
c
i
s
i
o
n
,
r
e
c
a
l
l
a
n
d
a
c
c
ur
a
c
y
i
n
r
i
c
e
p
l
a
n
t
l
e
a
f
d
i
s
e
a
s
e
r
e
c
o
gni
t
i
o
n
,
a
n
AI
-
b
a
s
e
d
a
tt
e
n
t
i
o
n
n
e
t
wor
k
a
n
d
s
e
m
a
n
t
i
c
b
a
t
c
h
n
o
r
m
a
li
z
e
d
De
e
pNe
t
(
A
N
-
S
B
ND
N)
i
s
de
s
i
g
n
e
d
us
i
ng
c
h
a
nne
l
do
t
pr
o
duc
t
a
tt
e
n
t
i
o
n
(
D
P
A
)
n
e
t
wor
k
-
b
a
s
e
d
pr
e
pr
o
c
e
s
s
i
n
g
a
n
d
s
e
m
a
n
t
i
c
r
e
g
i
o
n
o
f
i
n
t
e
r
e
s
t
(
R
OI
)
l
o
g
i
t
s
a
n
d
b
a
t
c
h
n
o
r
m
a
li
z
e
d
De
e
pN
e
t
f
e
a
t
ur
e
e
n
g
i
n
e
e
r
i
ng
.
R
e
s
t
o
f
t
h
e
m
a
n
us
c
r
i
pt
i
s
s
t
r
uc
t
u
r
e
d
a
s
g
i
ve
n
be
l
o
w.
I
n
s
e
c
t
i
o
n
2
g
i
ve
s
t
h
e
r
e
l
a
t
e
d
wor
ks
o
n
t
h
e
r
i
c
e
p
l
a
n
t
l
e
a
f
d
i
s
e
a
s
e
de
t
e
c
t
i
o
n
f
o
r
r
i
c
e
i
m
a
ge
s
.
S
e
c
t
i
o
n
3
d
i
s
p
l
a
y
s
c
o
n
c
i
s
e
e
x
p
l
a
n
a
t
i
o
n
o
f
A
I
-
b
a
s
e
d
A
N
-
S
B
ND
N.
Af
t
e
r
t
h
a
t
,
s
e
c
t
i
o
n
4
i
n
t
r
o
duc
e
s
e
x
pe
r
i
m
e
n
t
a
l
o
u
t
c
o
m
e
s
,
a
s
we
l
l
a
s
s
e
c
t
i
o
n
5
de
s
c
r
i
be
s
im
p
l
e
m
e
n
t
a
t
i
o
n
de
t
a
i
l
s
.
S
e
c
t
i
o
n
6
i
n
t
r
o
duc
e
s
a
c
o
m
pr
e
h
e
ns
i
ve
e
v
a
l
ua
t
i
o
n
a
n
a
ly
s
i
s
a
m
o
n
g
A
N
-
S
B
ND
N
m
e
t
h
o
d
a
n
d
ot
h
e
r
c
o
n
v
e
n
t
i
o
n
a
l
m
e
t
h
o
ds
us
i
n
g
t
a
bl
e
,
gr
a
phi
c
a
l
r
e
pr
e
s
e
n
t
a
t
i
o
n
.
L
a
s
t
l
y
,
s
e
c
t
i
o
n
7
c
o
n
c
l
ude
s
m
a
n
u
s
c
r
i
pt
.
2.
L
I
T
E
RA
T
UR
E
S
UR
VE
Y
R
i
c
e
i
s
a
n
e
s
s
e
n
t
i
a
l
f
o
o
d
s
o
ur
c
e
gl
o
b
a
ll
y
w
i
t
h
t
h
e
m
o
s
t
r
i
c
e
be
i
n
g
pr
o
duc
e
d
a
n
d
c
o
n
s
u
m
e
d
i
n
As
i
a
.
Ho
we
v
e
r
,
i
n
t
h
e
pr
e
s
e
n
c
e
o
f
f
u
n
g
i
,
b
a
c
t
e
r
i
a
a
n
d
ot
h
e
r
m
i
c
r
o
bi
a
l
d
i
s
e
a
s
e
s
po
s
e
a
n
e
ga
t
i
v
e
i
n
f
l
ue
n
c
e
o
n
t
h
e
p
l
a
n
t
’
s
h
e
a
l
t
h
a
n
d
c
r
o
p
y
i
e
l
d.
M
a
n
u
a
l
d
i
a
g
n
o
s
i
s
o
f
t
h
e
s
e
d
i
s
e
a
s
e
s
is
s
a
i
d
to
b
e
de
m
a
n
d
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ng
s
pe
c
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f
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c
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ll
y
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n
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r
e
a
s
w
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t
h
a
s
c
a
r
c
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t
y
o
f
c
r
o
p
pr
e
s
e
r
v
a
t
i
o
n
s
pe
c
i
a
li
s
t
s
.
A
uto
m
a
t
i
o
n
o
f
d
i
s
e
a
s
e
i
de
n
t
i
f
i
c
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t
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o
n
a
n
d
be
s
t
o
wi
n
g
e
f
f
o
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t
l
e
s
s
ly
a
c
c
e
s
s
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bl
e
d
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c
i
s
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o
n
-
s
uppo
r
t
m
e
c
h
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ni
s
m
s
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r
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pr
e
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e
qu
i
s
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t
e
f
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r
e
n
s
ur
i
ng
e
f
f
i
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[
12]
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[
14]
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[
15]
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[
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Evaluation Warning : The document was created with Spire.PDF for Python.
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’, sample images ‘
=
{
1
,
2
,
…
,
}
’
Output
: computationally
-
efficient noise
-
eliminated preprocessed rice plant leaf images
Step 1:
Initialize
‘
’, channel ‘
’
Step 2:
Begin
Step 3:
For
each Dataset ‘
’ with Sample Images ‘
’
Step 4: Obtain sample images as giv
en in equation (1)
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5:
Perform
Dot
Product
Attention
(DPA)
for
each
raw
sample
images
as
g
iven
in
equations (2), (3) and (4) to return sample image matrix, key matrix and value matrix
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6:
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Dot
Product
Attention
(DPA)
for
the
resultant
formu
lated
sample
image
matrix, key matrix and value matrix as given in equation (5)
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7:
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degree
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similarity
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func
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as
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If
‘
≥
0
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5
<
1
’
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Then
high cor
relation exists between ‘
’ and ‘
’ with respect to ‘
’
Step 10: Restore the foreground rice plant leaf image
Step 11: Return preprocessed image ‘
’
Step 12:
End if
Step 13:
If
‘
<
0
.
5
’
Step 14:
Then
low correlation exists between ‘
’ and ‘
’ with respect to ‘
’
Step 15: Discard the background portions
Step 16:
Go to
step 4
Step 17:
End for
Step 18:
End
3.
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2.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
I
S
S
N:
2252
-
8776
A
utomated
r
ice
lea
f
dis
e
as
e
de
tec
ti
on
us
ing
ar
ti
f
ici
al
int
e
ll
igenc
e
de
e
p
lear
ning
(
Suhail
a
M
.
P
.
)
411
Al
go
r
i
t
hm
2
.
S
e
m
a
n
t
i
c
R
OI
l
o
g
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t
s
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n
d
ba
t
c
h
n
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r
m
a
l
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z
e
d
De
e
pN
e
t
f
e
a
t
ur
e
e
n
g
i
ne
e
r
i
n
g
Input
: Dataset ‘
’
Output
: precise rice leaf disease detection
Step 1:
Initialize
‘
’, preprocessed image ‘
’, small batch ‘
’
Step 2:
Begin
//Region
-
of
-
Interest configuration
Step 3:
For
each Dataset ‘
’ with preprocessed image ‘
’
Step 4: Evaluate ground truth residual values as given in equations (8)
Step 5: Measure locus ‘
’ for a distance of ground truth ‘
’ as given in equation (9)
Step
6:
Evaluate
l
eft
and
right
edge
dista
nce
annotated
bounding
boxes
as
given
in
e
quation
(10)
Step 7:
End for
//Feature engineering
-
based disease detection
Step 8:
For
each small batch ‘
’
Step 9: Evaluate experimental mean and variance as given in equations (11) an
d (12)
Step 10: Perform normalization of
ROI
configured input separately as given in equation (13)
Step 11: Return engineered features ‘
(
)
’ and ‘
(
)
’ as given in equation (14)
Step
12:
Return
normalized
output
(i.e.,
p
lant
leaf
disease
in
rice
crop)
‘
(
)
’
as
given
in
equation (14)
Step 13:
End for
Step 14:
End
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1.
P
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as
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e
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ut
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t
.
RE
F
E
R
E
NC
E
S
[
1]
K
ir
t
i
a
nd
N
.
R
a
jp
a
l,
“
A
mul
ti
-
c
r
o
p
di
s
e
a
s
e
id
e
nt
i
f
ic
a
ti
o
n
a
ppr
o
a
c
h
ba
s
e
d
o
n
r
e
s
id
ua
l
a
tt
e
nt
i
o
n
l
e
a
r
ni
ng,”
J
our
nal
of
I
nt
e
ll
ig
e
nt
Sy
s
te
m
s
, v
o
l.
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. 2023, d
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y
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[
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S
.
S
a
r
a
s
w
a
t,
P
.
S
in
gh,
M
.
K
uma
r
,
a
nd
J
.
A
ga
r
w
a
l,
“
A
dv
a
nc
e
d
de
t
e
c
t
i
o
n
of
f
ungi
-
ba
c
te
r
ia
l
di
s
e
a
s
e
s
in
pl
a
nt
s
us
in
g
mo
di
f
i
e
d
d
e
e
p
ne
ur
a
l
n
e
tw
o
r
k
a
nd
D
S
U
R
F
,”
M
ul
t
ime
di
a
T
ool
s
and
A
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ic
at
io
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vo
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Ü
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A
ti
la
,
M
.
U
ç
a
r
,
K
.
A
k
y
ol
,
a
nd
E
.
U
ç
a
r
,
“
P
la
nt
l
e
a
f
di
s
e
a
s
e
c
la
s
s
if
i
c
a
ti
o
n
us
in
g
E
f
f
i
c
i
e
nt
N
e
t
d
e
e
p
l
e
a
r
ni
ng
m
o
d
e
l,
”
E
c
ol
og
ic
al
I
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m
at
ic
s
, v
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. 61, p. 10118
2, M
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P
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A
.
G
una
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a
n,
E
.
N
.
K
e
n
c
a
na
,
a
nd
K
.
S
a
r
i,
“
C
la
s
s
if
i
c
a
ti
o
n
of
r
ic
e
l
e
a
f
di
s
e
a
s
e
s
us
in
g
a
r
ti
f
i
c
ia
l
n
e
ur
a
l
n
e
tw
o
r
k,”
J
our
nal
of
P
hy
s
ic
s
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C
onf
e
r
e
nc
e
Se
r
ie
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6596/1722/
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Y
.
K
.
R
a
th
or
e
e
t
al
.
,
“
D
e
t
e
c
ti
o
n
of
r
i
c
e
pl
a
nt
di
s
e
a
s
e
f
r
o
m
R
G
B
a
nd
gr
a
y
s
c
a
le
im
a
ge
s
us
in
g
a
n
L
W
17
de
e
p
l
e
a
r
ni
ng
m
o
d
e
l,
”
E
le
c
tr
oni
c
R
e
s
e
ar
c
h A
r
c
hi
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e
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l.
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S
.
A
gg
a
r
w
a
l
e
t
al
.
,
“
R
ic
e
di
s
e
a
s
e
d
e
t
e
c
ti
o
n
us
in
g
a
r
ti
f
i
c
ia
l
i
nt
e
ll
ig
e
n
c
e
a
nd
ma
c
hi
n
e
l
e
a
r
ni
ng
t
e
c
hni
qu
e
s
t
o
im
pr
ovi
s
e
a
gr
o
-
bus
i
ne
s
s
,”
Sc
ie
nt
i
f
ic
P
r
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g
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[
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J
.
C
he
n,
A
.
Z
e
b,
Y
.
A
.
N
a
ne
hka
r
a
n,
a
nd
D
.
Z
ha
ng,
“
S
ta
c
ki
ng
e
ns
e
mbl
e
m
o
d
e
l
of
d
e
e
p
le
a
r
ni
ng
f
o
r
pl
a
nt
di
s
e
a
s
e
r
e
c
o
gni
t
i
on,”
J
our
nal
of
A
m
bi
e
nt
I
nt
e
ll
ig
e
nc
e
and
H
um
ani
z
e
d
C
om
put
in
g
,
vo
l.
14,
n
o
.
9,
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12372,
S
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10.1007/s
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S
.
V
a
ll
a
bha
jo
s
y
ul
a
,
V
.
S
is
tl
a
,
a
nd
V
.
K
.
K
.
K
o
ll
i,
“
T
r
a
ns
f
e
r
l
e
a
r
ni
ng
-
ba
s
e
d
d
e
e
p
e
ns
e
mbl
e
ne
u
r
a
l
ne
tw
o
r
k
f
or
pl
a
nt
le
a
f
di
s
e
a
s
e
d
e
t
e
c
ti
o
n,”
J
our
nal
of
P
la
nt
D
is
e
as
e
s
an
d
P
r
ot
e
c
ti
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K
.
P
.
A
s
ha
R
a
ni
a
nd
S
.
G
o
w
r
is
ha
nka
r
,
“
P
a
th
o
ge
n
-
ba
s
e
d
c
la
s
s
if
i
c
a
ti
o
n
of
p
la
nt
di
s
e
a
s
e
s
:
a
d
e
e
p
tr
a
ns
f
e
r
l
e
a
r
ni
ng
a
ppr
o
a
c
h
f
or
in
te
ll
ig
e
nt
s
upp
o
r
t
s
y
s
t
e
ms
,”
I
E
E
E
A
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M
.
S
ho
a
ib
e
t
al
.
,
“
A
n
a
d
v
a
n
c
e
d
de
e
p
l
e
a
r
ni
ng
m
o
de
ls
-
ba
s
e
d
pl
a
nt
di
s
e
a
s
e
d
e
t
e
c
ti
o
n
:
a
r
e
v
i
e
w
of
r
e
c
e
nt
r
e
s
e
a
r
c
h,”
F
r
on
ti
e
r
s
in
P
la
nt
Sc
ie
nc
e
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r
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s
.2023.1158
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M
.
C
hi
th
a
mba
r
a
th
a
nu
a
nd
M
.
K
.
J
e
y
a
kuma
r
,
“
S
ur
ve
y
o
n
c
r
o
p
p
e
s
t
de
te
c
ti
o
n
us
in
g
d
e
e
p
l
e
a
r
ni
ng
a
nd
ma
c
hi
n
e
l
e
a
r
ni
ng
a
p
pr
o
a
c
he
s
,”
M
ul
t
im
e
di
a
T
oo
l
s
an
d A
ppl
i
c
at
io
n
s
,
v
o
l.
82
,
no
.
27
, p
p
.
42
27
7
–
42
31
0,
N
o
v
.
20
23
,
do
i:
10
.1
00
7/
s
11
04
2
-
0
23
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1
52
21
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3.
[
12]
A
.
S
ha
r
ma
,
A
.
J
a
in
,
P
.
G
upt
a
,
a
nd
V
.
C
ho
w
da
r
y
,
“
M
a
c
hi
n
e
le
a
r
ni
ng
a
ppl
i
c
a
ti
o
ns
f
o
r
p
r
e
c
is
i
o
n
a
gr
i
c
ul
tu
r
e
:
a
c
o
mpr
e
h
e
n
s
iv
e
r
e
v
i
e
w
,”
I
E
E
E
A
c
c
e
s
s
, v
o
l.
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4873, 2021, d
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09/
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C
C
E
S
S
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M
.
A
gga
r
w
a
l,
V
.
K
hul
la
r
,
N
.
G
oy
a
l,
A
.
A
la
mm
a
r
i,
M
.
A
.
A
lb
a
ha
r
,
a
nd
A
.
S
in
gh,
“
L
ig
ht
w
e
ig
ht
f
e
d
e
r
a
t
e
d
l
e
a
r
ni
ng
f
o
r
r
i
c
e
le
a
f
di
s
e
a
s
e
c
la
s
s
if
i
c
a
ti
o
n
us
in
g
n
o
n
in
d
e
p
e
nde
nt
a
nd
id
e
nt
ic
a
ll
y
di
s
tr
ib
ut
e
d
im
a
g
e
s
,”
Sus
ta
in
abi
li
ty
(
S
w
it
z
e
r
la
nd)
,
vo
l.
15,
n
o
.
16,
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10.3390/s
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S
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H
.
A
be
d,
A
.
S
.
A
l
-
W
a
is
y
,
H
.
J
.
M
o
ha
mm
e
d,
a
nd
S
.
A
l
-
F
a
hda
w
i,
“
A
mo
de
r
n
d
e
e
p
l
e
a
r
ni
ng
f
r
a
me
w
o
r
k
in
r
o
b
o
t
vi
s
io
n
f
o
r
a
ut
o
ma
t
e
d
be
a
n
l
e
a
v
e
s
di
s
e
a
s
e
s
d
e
te
c
ti
o
n,”
I
nt
e
r
nat
io
nal
J
our
nal
of
I
nt
e
ll
ig
e
nt
R
obot
ic
s
and
A
ppl
ic
at
io
ns
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v
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l.
5,
n
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2,
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251, J
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Y
.
W
a
ng,
H
.
W
a
ng,
a
nd
Z
.
P
e
ng,
“
R
ic
e
di
s
e
a
s
e
s
d
e
t
e
c
t
i
o
n
a
nd
c
la
s
s
if
i
c
a
ti
o
n
us
in
g
a
tt
e
nt
i
o
n
ba
s
e
d
n
e
ur
a
l
n
e
tw
o
r
k
a
nd
ba
y
e
s
ia
n
o
pt
im
i
z
a
ti
o
n,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h A
ppl
ic
at
io
ns
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l.
178, p. 1
14770, S
e
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L
.
C
.
N
gugi
,
M
.
A
be
lwa
ha
b,
a
nd
M
.
A
bo
-
Z
a
hha
d,
“
R
e
c
e
nt
a
d
v
a
n
c
e
s
in
im
a
g
e
pr
o
c
e
s
s
in
g
te
c
hni
qu
e
s
f
o
r
a
ut
o
ma
t
e
d
le
a
f
p
e
s
t
a
nd
di
s
e
a
s
e
r
e
c
o
gni
ti
o
n
–
A
r
e
vi
e
w
,”
I
nf
or
m
at
io
n
P
r
oc
e
s
s
i
ng
in
A
gr
ic
ul
tu
r
e
,
vo
l.
8,
n
o
.
1,
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27
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M
a
r
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2
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pa
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V
.
G
a
ut
a
m
e
t
al
.
,
“
A
tr
a
n
s
f
e
r
le
a
r
ni
ng
-
ba
s
e
d
a
r
ti
f
i
c
ia
l
in
te
ll
ig
e
n
c
e
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o
d
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l
f
o
r
le
a
f
di
s
e
a
s
e
a
s
s
e
s
s
me
nt
,”
Sus
ta
in
ab
il
it
y
(
Sw
it
z
e
r
la
nd)
,
vo
l.
14, n
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2022, d
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A
.
K
.
S
in
gh,
S
.
V
.
N
.
S
r
e
e
ni
v
a
s
u,
U
.
S
.
B
.
K
.
M
a
ha
la
x
mi
,
H
.
S
ha
r
ma
,
D
.
D
.
P
a
ti
l,
a
nd
E
.
A
s
e
ns
o
,
“
H
y
br
id
f
e
a
tu
r
e
-
ba
s
e
d
di
s
e
a
s
e
de
t
e
c
t
i
o
n
in
pl
a
nt
le
a
f
us
in
g
c
o
n
vo
lu
ti
o
na
l
ne
u
r
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l
ne
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or
k,
ba
y
e
s
ia
n
o
pt
im
i
z
e
d
S
V
M
,
a
nd
r
a
ndo
m
f
or
e
s
t
c
l
a
s
s
if
i
e
r
,”
J
our
nal
of
F
ood Qualit
y
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l.
2022, pp. 1
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e
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S
.
S
.
H
a
r
a
ka
nna
na
v
a
r
,
J
.
M
.
R
uda
gi
,
V
.
I
.
P
ur
a
ni
kma
th
,
A
.
S
id
di
qua
,
a
nd
R
.
P
r
a
mo
dhi
n
i,
“
P
la
nt
l
e
a
f
di
s
e
a
s
e
d
e
t
e
c
ti
o
n
u
s
in
g
c
o
mput
e
r
v
is
i
o
n
a
nd
ma
c
hi
n
e
l
e
a
r
ni
ng
a
lg
o
r
it
hms
,”
G
lo
bal
T
r
ans
it
io
ns
P
r
oc
e
e
di
ngs
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v
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3,
n
o
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D
.
N
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v
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