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Science
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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J
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&
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p
Sci
,
Vo
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39
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
4
8
9
-
1
4
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8
1490
p
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[
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cr
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ter
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r
eliab
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.
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b
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m
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r
ev
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[
9
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id
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tifie
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tem
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d
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ep
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in
ar
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p
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—
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cla
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s
tr
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Ay
y
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ar
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a
l.
[
1
0
]
p
r
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p
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s
ed
th
e
class
if
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n
tech
n
iq
u
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f
co
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PV
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r
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r
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class
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as
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Fig
u
r
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2
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1
1
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p
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etailed
r
ev
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f
th
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im
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ity
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d
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ty
en
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en
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Sh
ar
m
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et
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l.
[
1
2
]
in
tr
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s
a
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v
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ataset
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d
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Fe
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[
1
3
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p
r
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1
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els
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9
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cu
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Ab
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k
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1
5
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ac
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v
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9
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[
1
6
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p
r
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a
r
ev
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2
7
8
p
ap
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
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1491
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u
r
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2
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Pre
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p
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m
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o
f
1
9
class
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d
ataset
[
1
0
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R
ah
m
a
et
a
l.
[
1
7
]
i
n
v
esti
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ates
th
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p
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t
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l.
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1
8
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p
r
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p
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ased
au
to
m
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r
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s
.
On
im
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a
l.
[
1
9
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s
tu
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in
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So
lNet,
a
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NN
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ch
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T
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lar
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Kar
ch
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l.
[
2
0
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p
r
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[
2
1
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p
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2
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m
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h
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9
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if
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h
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co
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wo
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k
a
r
e:
−
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o
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ANN
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ased
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e
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ased
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.
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I
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d
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J
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&
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p
Sci
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Vo
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39
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3
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Sep
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20
25
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1
4
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T
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o
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ch
m
ar
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ag
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SVM,
KNN,
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d
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NN
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o
d
els.
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y
a
d
d
r
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g
lim
itatio
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th
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o
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2
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iews
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elate
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k
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tio
n
3
o
u
tlin
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th
e
p
r
o
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o
s
ed
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eth
o
d
o
lo
g
y
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s
ec
tio
n
4
p
r
esen
ts
th
e
r
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lts
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d
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s
s
io
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an
d
s
ec
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5
co
n
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d
es
with
k
ey
f
in
d
in
g
s
an
d
f
u
tu
r
e
d
ir
ec
tio
n
s.
2.
M
E
T
H
O
D
T
h
e
class
if
icatio
n
o
f
d
is
tin
ct
t
y
p
es
o
f
d
e
b
r
is
th
at
a
r
e
f
o
r
m
e
d
o
n
s
o
lar
PV
a
r
r
ay
s
ca
n
b
e
ac
h
iev
ed
b
y
th
e
f
o
llo
win
g
s
tep
s
s
h
o
wn
in
Fig
u
r
e
3
.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
e
n
co
m
p
ass
es
a
th
r
e
e
-
s
tag
e
p
r
o
ce
s
s
f
o
r
im
ag
e
class
if
icatio
n
:
th
e
f
ir
s
t
s
tep
is
im
ag
e
ac
q
u
is
itio
n
an
d
p
r
e
-
p
r
o
ce
s
s
in
g
to
en
s
u
r
e
d
ata
s
tan
d
ar
d
izatio
n
an
d
q
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ality
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h
e
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n
d
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e
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s
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ain
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o
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ac
h
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e
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r
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o
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ich
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er
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es
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e
ex
tr
ac
ted
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ea
tu
r
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if
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im
ag
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.
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h
ese
s
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s
en
s
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r
e
a
s
tr
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ctu
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ed
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i
p
elin
e
f
o
r
ac
cu
r
ate
d
e
b
r
is
class
if
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.
Fig
u
r
e
3
.
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s
tem
m
o
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el
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o
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cl
ass
if
icatio
n
o
f
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io
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eb
r
is
o
n
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o
lar
PV
ar
r
ay
s
2
.
1
.
I
ma
g
e
a
cquis
it
io
n a
nd
p
re
pro
ce
s
s
ing
T
h
e
d
ataset
p
r
o
v
id
es
d
ep
o
s
its
with
th
e
f
o
llo
win
g
class
es:
“
W
ith
o
u
t
d
u
s
t
”
,
“
B
ir
d
d
r
o
p
p
in
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s
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,
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C
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u
s
t
”
,
“
Dr
y
leav
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”
an
d
1
5
m
o
r
e
u
n
i
q
u
e
class
es
s
h
o
wn
in
T
ab
le
2
.
T
h
e
d
ataset
u
s
ed
co
n
ta
in
s
a
to
tal
o
f
1
2
2
2
im
ag
es wh
ich
ar
e
d
iv
id
ed
in
to
1
9
class
es.
T
o
en
s
u
r
e
co
n
s
is
te
n
cy
,
all
im
ag
es we
r
e
r
esized
t
o
a
f
ix
ed
r
eso
lu
tio
n
o
f
2
5
6
x
2
5
6
p
ix
els
an
d
u
n
d
er
wen
t
c
r
o
p
p
in
g
t
o
r
em
o
v
e
ir
r
ele
v
an
t
b
ac
k
g
r
o
u
n
d
in
f
o
r
m
atio
n
.
T
h
is
s
tan
d
ar
d
izatio
n
is
ess
en
tial f
o
r
ef
f
icien
t f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
class
if
i
ca
tio
n
.
Pre
-
p
r
o
ce
s
s
in
g
was
an
ess
en
tial
s
tep
to
en
h
an
ce
tex
tu
r
al
in
f
o
r
m
atio
n
an
d
im
p
r
o
v
e
t
h
e
r
eli
ab
ilit
y
o
f
th
e
co
llected
d
ata,
as
th
e
p
r
o
p
o
s
ed
class
if
icatio
n
m
eth
o
d
u
s
es
tex
tu
r
e
f
ea
t
u
r
es.
T
h
is
s
tep
in
v
o
lv
e
d
co
n
v
er
tin
g
th
e
in
p
u
t
im
ag
es
f
r
o
m
th
e
s
tan
d
ar
d
R
G
B
to
HSV
co
lo
r
f
o
r
m
at.
HSV
is
c
h
o
s
en
d
u
e
t
o
its
r
o
b
u
s
tn
ess
to
ch
an
g
es
in
lig
h
tin
g
co
n
d
itio
n
s
,
m
ak
in
g
it
s
u
itab
le
f
o
r
r
ea
l
-
w
o
r
ld
a
p
p
licatio
n
s
.
His
to
g
r
am
-
b
ased
th
r
esh
o
l
d
in
g
was
ap
p
lied
to
s
eg
m
e
n
t
d
e
b
r
i
s
f
r
o
m
th
e
p
an
el
b
ac
k
g
r
o
u
n
d
,
en
s
u
r
in
g
th
at
th
e
ex
tr
ac
ted
f
ea
tu
r
es
co
r
r
esp
o
n
d
ac
cu
r
ately
to
co
n
tam
in
a
n
ts
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
o
v
er
all
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
u
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ed
in
th
e
s
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d
y
.
I
t
p
r
o
v
id
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a
d
etailed
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v
er
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iew
o
f
h
o
w
r
aw
im
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p
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s
ed
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d
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lo
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s
p
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ce
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n
v
e
r
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a
n
d
th
r
esh
o
ld
in
g
,
to
en
h
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ce
f
e
at
u
r
e
ex
tr
ac
tio
n
.
Fig
u
r
e
4
(
a)
s
h
o
ws
a
s
am
p
le
im
ag
e
with
Fig
u
r
e
4
(
b
)
s
p
ec
if
ically
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ig
h
lig
h
ts
th
e
h
is
to
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r
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ase
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ased
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ets.
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l
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3
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f
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l
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:
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l
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3.
RE
SU
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S AN
D
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h
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o
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3
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1
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ich
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e
s
u
m
m
ar
i
ze
d
in
T
ab
le
4
.
Fig
u
r
e
5
illu
s
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ates
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o
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if
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r
ese
n
ts
th
e
class
-
wis
e
ac
cu
r
ac
y
p
er
f
o
r
m
a
n
ce
,
wh
ile
Fig
u
r
e
5
(
b
)
d
is
p
lay
s
th
e
co
n
f
u
s
io
n
m
atr
ix
f
o
r
f
u
r
t
h
er
an
aly
s
i
s
.
T
h
e
class
-
wis
e
ac
cu
r
ac
y
d
is
tr
ib
u
tio
n
h
i
g
h
lig
h
ts
th
e
s
tr
en
g
th
s
an
d
lim
itatio
n
s
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
s
h
o
wca
s
in
g
v
ar
iatio
n
s
i
n
m
o
d
el
p
r
ed
ictio
n
s
ac
r
o
s
s
d
if
f
er
en
t
d
eb
r
is
ty
p
es.
T
h
is
an
aly
s
is
p
r
o
v
id
es
in
s
ig
h
ts
in
to
m
is
clas
s
if
icatio
n
tr
en
d
s
an
d
ar
e
as
r
eq
u
ir
in
g
f
u
r
th
er
o
p
tim
izatio
n
,
en
s
u
r
in
g
a
m
o
r
e
ef
f
ec
tiv
e
d
ep
lo
y
m
e
n
t
o
f
th
e
au
to
m
ated
d
eb
r
is
d
etec
tio
n
f
r
am
ewo
r
k
.
T
h
e
ANN
m
o
d
el
d
em
o
n
s
tr
ated
th
e
h
ig
h
e
s
t
class
if
icatio
n
ac
cu
r
ac
y
(
9
3
.
9
4
%),
o
u
tp
er
f
o
r
m
i
n
g
t
r
ad
itio
n
al
m
ac
h
in
e
lear
n
in
g
m
o
d
els
s
u
ch
as
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
an
d
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
.
T
h
e
cl
ass
-
wis
e
ac
cu
r
ac
y
d
is
tr
ib
u
tio
n
h
ig
h
lig
h
ts
th
e
s
tr
e
n
g
th
s
an
d
lim
itatio
n
s
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
s
h
o
wca
s
in
g
v
ar
iatio
n
s
in
m
o
d
el
p
r
ed
ictio
n
s
ac
r
o
s
s
d
if
f
e
r
en
t d
e
b
r
is
ty
p
es.
T
h
e
ex
tr
ac
te
d
f
ea
t
u
r
es
ar
e
a
ls
o
r
an
k
e
d
to
d
eter
m
i
n
e
wh
ich
f
ea
tu
r
e
d
o
m
in
ates
m
o
r
e.
Fig
u
r
e
6
illu
s
tr
ates
th
e
r
an
k
in
g
o
f
all
4
1
f
ea
tu
r
es
th
r
o
u
g
h
th
e
m
in
i
m
u
m
r
ed
u
n
d
an
c
y
m
ax
im
u
m
r
elev
an
ce
(
m
R
MR)
alg
o
r
ith
m
.
I
t
h
ig
h
lig
h
ts
th
e
to
p
r
an
k
ed
f
ea
tu
r
es
ex
tr
ac
ted
u
s
in
g
m
R
MR
s
h
o
wca
s
in
g
th
o
s
e
th
at
co
n
tr
ib
u
te
th
e
m
o
s
t sig
n
if
ican
t v
alu
e
to
class
if
icatio
n
ac
cu
r
ac
y
.
T
ab
le
4
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
o
f
m
ac
h
in
e
lear
n
i
n
g
m
o
d
e
ls
M
L
m
o
d
e
l
A
c
c
u
r
a
c
y
(
%)
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
sc
o
r
e
ANN
9
3
.
9
4
%
0
.
9
4
0
.
9
4
0
.
9
4
S
V
M
8
4
.
0
8
%
0
.
8
5
0
.
8
4
0
.
8
4
K
N
N
_
R
F
8
9
.
8
0
%
0
.
9
1
0
.
9
0
0
.
9
0
K
N
N
7
6
.
3
3
%
0
.
7
9
0
.
7
6
0
.
7
6
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
C
o
mp
r
eh
en
s
ive
mu
lticla
s
s
d
e
b
r
is
d
etec
tio
n
fo
r
s
o
la
r
p
a
n
el
ma
in
ten
a
n
ce
u
s
in
g
A
N
N
… (
R
en
u
ka
Dev
i S
.
M
.
)
1495
(
a)
(
b
)
Fig
u
r
e
5
.
ANN
Mo
d
el
p
r
ed
icti
o
n
s
(a
)
class
wis
e
p
er
f
o
r
m
an
ce
an
d
(
b
)
co
n
f
u
s
io
n
m
atr
ix
Fig
u
r
e
6
.
R
an
k
in
g
o
f
4
1
f
ea
tu
r
es b
ased
o
n
f
ea
tu
r
e
s
co
r
e
3
.
2
.
I
nte
rpre
t
a
t
io
n
o
f
re
s
ults a
nd
co
m
pa
riso
n wit
h o
t
her
s
t
ud
ies
T
h
e
h
ig
h
ac
cu
r
ac
y
o
f
th
e
ANN
m
o
d
el
s
u
g
g
ests
th
at
a
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
is
well
-
s
u
ited
f
o
r
d
eb
r
is
class
if
icatio
n
in
s
o
lar
p
an
els.
Ho
wev
er
,
ce
r
tain
d
eb
r
is
ca
teg
o
r
ies,
s
u
ch
as
“
San
d
”
an
d
“
B
ir
d
Dr
o
p
p
in
g
s
,
”
s
h
o
wed
lo
wer
class
if
icatio
n
ac
cu
r
ac
y
d
u
e
to
th
eir
s
im
ilar
ity
in
tex
tu
r
e
an
d
co
lo
u
r
with
o
th
er
class
es.
T
h
ese
f
in
d
in
g
s
ar
e
co
n
s
is
ten
t
with
p
r
ev
io
u
s
s
tu
d
ies,
wh
ich
also
r
ep
o
r
ted
ch
allen
g
es
in
d
is
tin
g
u
is
h
in
g
v
is
u
ally
s
im
ilar
co
n
tam
in
an
ts
o
n
PV
m
o
d
u
les.
I
n
co
m
p
ar
is
o
n
,
s
tu
d
ies
s
u
ch
as
[
2
6
]
-
[
2
8
]
ex
p
lo
r
e
d
co
n
v
en
t
io
n
al
m
ac
h
in
e
lear
n
in
g
m
o
d
els
(
e.
g
.
,
SVM
an
d
d
e
cisi
o
n
tr
ee
s
)
an
d
ac
h
iev
ed
lo
wer
ac
cu
r
ac
y
lev
els
(
8
0
-
9
3
%).
Ou
r
r
esu
lts
r
ein
f
o
r
c
e
th
at
in
teg
r
atin
g
tex
tu
r
e
-
b
ase
d
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
d
ee
p
l
ea
r
n
in
g
tech
n
i
q
u
es
s
ig
n
if
ican
tly
en
h
a
n
ce
s
class
if
i
ca
tio
n
p
er
f
o
r
m
an
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
4
8
9
-
1
4
9
8
1496
3
.
3
.
Study
lim
it
a
t
io
ns
a
nd
f
uture
s
co
pe
Desp
ite
th
e
p
r
o
m
is
in
g
r
esu
lts
,
s
o
m
e
lim
itatio
n
s
ex
is
t in
th
e
s
tu
d
y
:
−
Data
s
et
s
ize
an
d
d
iv
er
s
ity
–
T
h
e
d
ataset
co
n
tain
s
1
2
2
2
im
ag
es,
b
u
t
i
n
co
r
p
o
r
atin
g
m
o
r
e
d
i
v
er
s
e
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
(
e.
g
.
,
wet
p
an
els,
v
ar
y
in
g
lig
h
t in
te
n
s
ity
)
co
u
ld
im
p
r
o
v
e
g
en
e
r
aliza
tio
n
.
−
Misclas
s
if
icatio
n
in
Similar
C
lass
es
–
C
er
tain
d
eb
r
is
ca
teg
o
r
ies
r
eq
u
ir
e
ad
v
a
n
ce
d
f
ea
tu
r
e
ex
tr
ac
tio
n
o
r
ad
d
itio
n
al
p
r
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es to
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
.
−
R
ea
l
-
tim
e
i
m
p
lem
en
tatio
n
–
W
h
ile
th
e
cu
r
r
en
t
ANN
m
o
d
e
l
p
er
f
o
r
m
s
well
in
ex
p
er
im
en
t
al
s
ettin
g
s
,
its
d
ep
lo
y
m
e
n
t in
a
r
ea
l
-
tim
e
s
o
lar
p
an
el
m
o
n
ito
r
in
g
s
y
s
tem
n
ee
d
s
f
u
r
th
er
v
alid
atio
n
.
Fu
tu
r
e
wo
r
k
will f
o
cu
s
o
n
:
−
E
x
p
an
d
i
n
g
th
e
d
ataset
with
au
g
m
en
ted
a
n
d
r
ea
l
-
w
o
r
ld
im
ag
e
s
f
r
o
m
m
u
ltip
le
PV in
s
tallatio
n
s
.
−
T
esti
n
g
r
ea
l
-
tim
e
in
f
er
e
n
ce
u
s
i
n
g
ed
g
e
co
m
p
u
tin
g
h
ar
d
war
e
f
o
r
o
n
-
s
ite
im
p
lem
en
tatio
n
.
−
I
n
teg
r
atin
g
s
p
ec
tr
al
im
ag
in
g
tech
n
iq
u
es to
d
if
f
er
e
n
tiate
d
eb
r
i
s
ty
p
es m
o
r
e
ef
f
ec
tiv
el
y
.
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
ex
p
lo
r
e
d
th
e
p
o
ten
t
ial
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
es
f
o
r
au
to
m
ated
d
eb
r
is
d
et
ec
tio
n
an
d
class
if
icatio
n
o
n
s
o
lar
p
a
n
els.
T
h
e
ex
p
er
im
en
tal
r
esu
lts
d
em
o
n
s
tr
ated
t
h
at
th
e
ANN
m
o
d
el
ac
h
iev
ed
th
e
h
ig
h
est
class
if
icatio
n
ac
cu
r
ac
y
o
f
9
3
.
9
4
%,
s
ig
n
if
ican
t
ly
o
u
t
p
er
f
o
r
m
in
g
c
o
n
v
e
n
tio
n
al
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
els
s
u
ch
as
SVM
an
d
KNN
.
T
h
e
f
in
d
in
g
s
h
ig
h
lig
h
t
th
at
i
n
teg
r
atin
g
tex
tu
r
e
-
b
ased
f
ea
t
u
r
e
e
x
tr
ac
tio
n
with
d
ee
p
lear
n
in
g
tech
n
iq
u
es
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
T
h
is
s
tu
d
y
p
r
o
v
i
d
es
a
s
c
alab
le
an
d
ef
f
icien
t
f
r
am
ew
o
r
k
t
h
at
ca
n
f
ac
ilit
ate
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
an
d
m
ain
te
n
an
ce
o
f
PV
s
y
s
tem
s
,
r
ed
u
cin
g
en
e
r
g
y
lo
s
s
es
d
u
e
to
d
eb
r
is
ac
cu
m
u
latio
n
.
Desp
ite
th
e
p
r
o
m
is
in
g
r
esu
lts
,
ce
r
tain
ch
allen
g
es
r
em
ain
.
Fu
tu
r
e
s
tu
d
ies
will
in
teg
r
ate
r
ea
l
-
wo
r
ld
im
a
g
es
ca
p
tu
r
ed
u
n
d
er
v
ar
ied
e
n
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
s
u
ch
as
f
o
g
,
r
ai
n
,
an
d
ex
tr
em
e
s
u
n
lig
h
t
to
e
n
h
an
ce
m
o
d
el
r
o
b
u
s
tn
ess
.
Ad
d
itio
n
all
y
,
th
e
class
if
icatio
n
o
f
s
im
ilar
-
lo
o
k
in
g
d
eb
r
is
ty
p
es
(
e.
g
.
,
s
an
d
an
d
r
e
d
s
o
il)
r
e
m
ain
s
a
ch
allen
g
e
th
at
co
u
ld
b
e
a
d
d
r
ess
ed
t
h
r
o
u
g
h
s
p
ec
tr
al
im
a
g
in
g
tech
n
iq
u
es
o
r
im
p
r
o
v
e
d
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
.
T
h
e
p
r
o
p
o
s
ed
ANN
m
o
d
el
h
as
th
e
p
o
ten
tial
to
b
e
d
ep
lo
y
e
d
in
s
m
ar
t
s
o
lar
f
ar
m
s
,
in
teg
r
atin
g
with
I
o
T
-
b
ased
m
o
n
ito
r
in
g
s
y
s
te
m
s
to
en
ab
le
a
u
to
m
ated
d
e
b
r
is
d
etec
tio
n
an
d
clea
n
in
g
.
B
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1497
DATA AV
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RE
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NC
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[
1
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A
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A
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M
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