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ts
,
m
ai
n
te
n
a
n
ce
a
n
d
i
m
p
r
o
v
in
g
g
en
er
aliza
t
io
n
.
T
h
e
P
C
A
,
KP
C
A
,
an
d
I
C
A
ar
e
th
r
ee
i
m
p
o
r
tan
t
f
ea
t
u
r
e
ex
tr
ac
tio
n
tec
h
n
iq
u
es
u
s
ed
f
o
r
d
i
m
en
s
io
n
al
it
y
r
ed
u
ctio
n
a
n
d
w
o
r
k
o
n
s
el
f
-
o
r
g
an
izi
n
g
u
n
s
u
p
er
v
is
ed
lear
n
i
n
g
ap
p
r
o
ac
h
.
W
e
u
s
ed
all
th
e
s
e
t
h
r
ee
tec
h
n
iq
u
e
s
(
P
C
A
,
KP
C
A
,
an
d
I
C
A
)
f
o
r
d
ata
r
ed
u
ctio
n
an
d
clas
s
i
f
icatio
n
.
T
h
e
m
o
s
t
e
f
f
ec
ti
v
e
a
n
d
s
i
m
p
l
e
m
u
lti
v
ar
iate
a
n
al
y
s
i
s
b
ased
to
ei
g
e
n
v
ec
to
r
i
s
“
P
r
in
cip
al
C
o
m
p
o
n
en
t
An
al
y
s
i
s
”.
P
C
A
a
n
o
r
th
o
g
o
n
a
l
lin
ea
r
tr
an
s
f
o
r
m
atio
n
u
s
ed
to
co
n
v
er
t
th
e
d
ata
to
a
n
e
w
co
o
r
d
in
ate
s
y
s
te
m
.
I
t
also
u
s
ed
f
o
r
s
i
g
n
al
p
r
o
ce
s
s
i
n
g
an
d
d
ata
r
ed
u
ctio
n
in
s
tatis
t
ical
p
atter
n
r
ec
o
g
n
itio
n
.
Self
-
o
r
g
an
ized
lear
n
i
n
g
p
r
o
v
id
es
an
ad
ap
tiv
e
alg
o
r
it
h
m
f
o
r
P
C
A
.
I
t
m
a
x
i
m
izes
th
e
r
ate
o
f
d
ec
r
ea
s
e
o
f
v
a
r
ian
ce
u
s
in
g
E
i
g
e
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
R
ec
o
g
n
itio
n
o
f To
ma
to
La
te
B
lig
h
t b
y
Usi
n
g
DW
T a
n
d
C
o
mp
o
n
en
t A
n
a
lysi
s
(
Hite
s
h
w
a
r
i S
a
b
r
o
l)
197
d
ec
o
m
p
o
s
itio
n
m
eth
o
d
o
r
v
alu
e
d
ec
o
m
p
o
s
itio
n
t
h
eo
r
e
m
.
W
e
h
av
e
u
s
ed
Sin
g
u
lar
v
a
lu
e
d
ec
o
m
p
o
s
itio
n
th
eo
r
e
m
b
etter
t
h
e
n
E
i
g
en
d
ec
o
m
p
o
s
i
tio
n
.
I
n
m
ath
e
m
at
ical
t
er
m
s
,
ca
l
c
u
late
th
e
e
ig
e
n
v
alu
e
s
o
f
t
h
e
co
v
ar
ian
c
e
m
atr
i
x
o
f
t
h
e
o
r
ig
i
n
al
o
u
tp
u
ts
.
P
C
A
li
n
ea
r
l
y
tr
an
s
f
o
r
m
a
h
i
g
h
-
d
i
m
en
s
io
n
al
s
p
ac
e
w
h
ich
is
u
n
co
r
r
elate
d
an
d
o
r
th
o
g
o
n
al.
First,
co
m
p
u
te
t
h
e
eig
e
n
v
alu
e
s
a
n
d
ei
g
en
v
ec
to
r
s
,
th
e
n
s
o
r
t
t
h
e
ei
g
e
n
v
al
u
es
i
n
d
esce
n
d
in
g
o
r
d
er
an
d
ig
n
o
r
e
th
e
r
ea
ll
y
s
m
all
v
alu
e
s
.
T
h
en
w
e
tr
an
s
f
o
r
m
d
ata
in
to
th
e
eig
e
n
s
p
ac
e
f
o
r
m
ed
b
y
th
e
s
elec
ted
eig
en
v
ec
to
r
.
T
h
e
m
ea
n
v
ec
to
r
o
f
th
e
p
o
p
u
latio
n
i
s
d
ef
i
n
ed
as
:
∑
(
2
)
T
o
f
in
d
th
e
co
v
ar
ia
n
ce
m
atr
ix
∑
(
3
)
w
h
er
e
C
x
is
th
e
co
v
ar
ia
n
ce
,
K
is
th
e
s
a
m
p
les
f
r
o
m
r
an
d
o
m
v
al
u
es,
a
n
d
(
x
-
m
x
)(x
-
m
x
)
T
ar
e
th
e
n
*
n
o
r
d
er
o
f
m
atr
ices
.
(
4
)
w
h
er
e
A
is
t
h
e
tr
an
s
f
o
r
m
at
io
n
m
atr
i
x
to
m
ap
x
i
n
to
v
ec
to
r
y
a
n
d
th
e
d
iag
o
n
al
m
atr
i
x
i
s
C
y
an
d
co
n
tai
n
i
n
g
ele
m
e
n
ts
w
it
h
t
h
e
m
ai
n
d
iag
o
n
al
ar
e
th
e
eig
e
n
v
al
u
es
o
f
C
x
.
T
h
e
m
atr
i
x
A
k
i
s
d
esig
n
ed
f
r
o
m
k
eig
e
n
v
ec
to
r
s
eq
u
iv
ale
n
t
to
t
h
e
k
lar
g
est
ei
g
en
v
al
u
e
s
,
p
r
o
d
u
cin
g
a
tr
an
s
f
o
r
m
at
io
n
m
atr
ix
o
f
o
r
d
er
k
*
n
.
T
h
e
r
ec
o
n
s
tr
u
ctio
n
o
f
v
ec
to
r
b
y
A
k
is
:
̂
(
5
)
T
h
e
ke
r
n
el
b
ased
m
e
th
o
d
u
s
e
d
f
o
r
n
o
n
li
n
ea
r
P
C
A
is
a
Ker
n
el
P
r
in
cip
al
C
o
m
p
o
n
e
n
t
An
a
l
y
s
i
s
.
T
h
e
k
er
n
el
m
et
h
o
d
is
u
s
ed
to
tr
an
s
f
o
r
m
s
t
h
e
o
r
ig
in
al
i
n
p
u
ts
i
n
t
o
h
ig
h
d
i
m
e
n
s
io
n
al
f
ea
t
u
r
e
s
p
ac
e
(
in
n
er
p
r
o
d
u
ct
k
er
n
el)
,
w
h
er
e
t
h
e
co
m
p
u
tatio
n
p
er
f
o
r
m
ed
o
n
f
ea
t
u
r
e
s
p
ac
e
w
h
ich
i
s
n
o
n
-
l
in
ea
r
l
y
r
elate
d
to
in
p
u
t
s
p
ac
e.
I
n
Ker
n
el
P
C
A
,
th
e
r
elatio
n
b
et
w
ee
n
in
p
u
t
s
p
ac
e
an
d
f
ea
tu
r
e
s
p
ac
e
is
n
o
n
li
n
ea
r
b
u
t
i
m
p
lem
en
tatio
n
r
elie
s
o
n
lin
ea
r
al
g
eb
r
a,
s
o
w
e
t
h
in
k
t
h
at
it’s
a
n
ex
ten
s
io
n
o
f
o
r
d
in
ar
y
P
C
A
.
I
n
t
h
is
s
t
u
d
y
,
w
e
w
e
r
e
u
s
i
n
g
Ga
u
s
s
ian
-
t
y
p
e
k
er
n
el.
Ma
t
h
e
m
atica
ll
y
,
w
e
w
o
u
ld
li
k
e
to
f
in
d
d
ir
ec
ti
o
n
v
ec
to
r
in
th
e
f
ea
tu
r
e
s
p
ac
e.
A
ll
th
e
m
atter
s
r
e
m
ain
s
a
m
e
a
s
in
P
C
A
,
b
u
t
with
k
er
n
el
m
atr
ix
〈
(
)
(
)
〉
(
)
(
6
)
T
h
e
p
r
o
j
ec
tio
n
o
n
to
th
e
o
p
tim
al
d
ir
ec
tio
n
is
〈
(
)
〉
〈
(
)
∑
(
)
〉
∑
(
)
(
7
)
I
n
d
ep
en
d
en
t
C
o
m
p
o
n
e
n
t
An
al
y
s
i
s
:
(
C
ar
d
o
aso
2
0
0
3
)
s
tated
t
h
at
“
I
n
d
ep
en
d
en
t
C
o
m
p
o
n
en
t
An
al
y
s
i
s
is
t
h
e
d
ec
o
m
p
o
s
itio
n
o
f
r
an
d
o
m
v
ec
to
r
i
n
to
li
n
ea
r
co
m
p
o
n
e
n
ts
th
a
t
ar
e
s
tatis
t
icall
y
i
n
d
ep
en
d
en
t
a
s
p
o
s
s
ib
le,
w
h
er
e
t
h
e
ter
m
in
d
ep
en
d
e
n
ce
is
u
n
d
er
s
to
o
d
i
n
i
ts
s
tr
o
n
g
est
s
tatis
tical
s
e
n
s
e;
I
C
A
g
o
es
b
e
y
o
n
d
(
s
ec
o
n
d
o
r
d
er
)
d
ec
o
r
r
elatio
n
an
d
th
er
e
f
o
r
e
r
eq
u
ir
es
t
h
at
t
h
e
o
b
s
er
v
at
io
n
s
r
ep
r
esen
tin
g
th
e
d
ata
v
ec
to
r
b
e
n
o
n
-
Ga
u
s
s
ian
”[
2
0
]
.
I
n
th
e
s
t
u
d
y
,
w
e
u
s
ed
w
h
it
en
in
g
a
s
a
p
r
ep
r
o
ce
s
s
in
g
s
t
ep
f
o
r
I
C
A
a
lg
o
r
it
h
m
to
f
i
n
d
th
e
u
n
co
r
r
elate
d
co
m
p
o
n
e
n
t
s
b
u
t
n
ee
d
n
o
t
b
e
i
n
d
ep
en
d
en
t,
a
n
d
th
e
v
ar
ia
n
ce
is
eq
u
al
to
t
h
e
u
n
i
t
y
.
Ne
x
t,
a
p
p
ly
d
is
cr
ete
Haar
w
a
v
elet
tr
a
n
s
f
o
r
m
atio
n
to
ex
tr
ac
t
w
a
v
elet
f
ea
tu
r
e
s
.
3.
E
VA
L
UA
T
I
O
N
W
e
u
s
e
th
e
E
u
clid
ea
n
Dis
ta
n
c
e
m
e
t
h
o
d
to
m
ea
s
u
r
e
th
e
d
is
ta
n
ce
b
et
w
ee
n
th
e
tr
ai
n
i
n
g
s
e
t
an
d
test
i
n
g
s
et.
E
ith
er
t
h
r
o
u
g
h
s
u
p
er
v
i
s
e
d
lear
n
in
g
o
r
u
n
s
u
p
er
v
is
ed
l
ea
r
n
in
g
ar
e
k
n
o
w
n
,
a
n
u
n
k
n
o
w
n
o
b
j
ec
t,
m
a
y
b
e
ca
lled
test
o
b
j
ec
t
ca
n
r
ec
o
g
n
i
ze
b
y
ass
o
ciat
in
g
it
w
it
h
o
n
e
o
f
th
o
s
e
clas
s
u
s
i
n
g
s
i
m
ilar
i
t
y
(
o
r
in
o
th
er
w
o
r
d
s
,
d
is
s
i
m
ilar
it
y
o
r
d
is
tan
ce
)
m
ea
s
u
r
es.
T
o
m
ea
s
u
r
e
th
e
d
i
s
s
i
m
i
lar
it
y
o
r
d
is
tan
ce
o
f
t
w
o
cla
s
s
es
i.e
.
T
o
m
ato
late
b
lig
h
t i
n
f
ec
ted
o
r
h
ea
lth
y
i
m
a
g
es,
w
e
u
s
ed
E
u
clid
ea
n
Dis
ta
n
ce
m
ea
s
u
r
e
as f
o
llo
w
i
n
g
√
∑
(
)
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
7
:
1
9
4
–
1
9
9
198
Usi
n
g
P
C
A
an
d
KP
C
A
,
w
e
m
ea
s
u
r
e
th
e
d
is
ta
n
ce
b
et
w
ee
n
eig
e
n
v
al
u
e
o
f
tr
ain
in
g
d
ata
an
d
test
in
g
d
ata.
I
n
th
e
ca
s
e
o
f
I
C
A
,
t
h
e
d
is
tan
ce
m
ea
s
u
r
ed
b
et
w
ee
n
eig
en
v
ec
to
r
co
m
p
u
ted
.
I
f
t
h
e
d
is
t
an
ce
is
less
f
r
o
m
a
p
ar
ticu
lar
class
,
t
h
en
t
h
e
test
in
g
i
m
a
g
e
b
elo
n
g
s
to
t
h
a
t c
las
s
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
r
ec
o
g
n
itio
n
an
d
cla
s
s
i
f
ica
tio
n
o
f
to
m
a
to
late
b
lig
h
t
b
y
u
s
in
g
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
atio
n
a
n
d
co
m
p
o
n
e
n
t
an
a
l
y
s
is
y
ie
ld
ed
ac
cu
r
ac
y
9
6
.
4
%.
T
h
e
P
C
A
,
KP
C
A
,
an
d
I
C
A
ar
e
u
s
ed
t
o
th
e
r
ed
u
ct
io
n
o
f
d
i
m
en
s
io
n
s
o
f
th
e
d
ata
u
s
i
n
g
w
a
v
elet
f
ea
t
u
r
es
a
n
d
t
h
en
th
ese
tec
h
n
iq
u
es
u
s
ed
f
o
r
t
o
m
a
to
p
lan
t
d
i
s
ea
s
e
r
ec
o
g
n
itio
n
an
d
class
i
f
icatio
n
p
u
r
p
o
s
e
.
T
h
e
tw
o
clas
s
es
i.e
.
late
b
lig
h
t
in
f
ec
ted
an
d
h
ea
lt
h
y
u
s
ed
to
class
i
f
y
w
h
et
h
er
th
e
i
m
a
g
e
is
a
f
f
ec
ted
by
late
b
li
g
h
t
d
is
ea
s
e
o
r
n
o
t.
T
o
ca
lc
u
latin
g
t
h
e
m
i
n
i
m
u
m
d
is
tan
ce
b
et
w
ee
n
tr
ain
i
n
g
a
n
d
te
s
ti
n
g
d
ata,
E
u
c
lid
ea
n
d
is
ta
n
ce
m
ea
s
u
r
e
w
a
s
u
s
ed
to
cla
s
s
i
f
y
t
h
at
te
s
ti
n
g
i
m
ag
e
is
b
elo
n
g
s
to
w
h
ic
h
class
i
n
f
ec
ted
o
r
h
ea
lth
y
.
T
a
b
le
1
.
s
h
o
w
s
th
e
co
m
p
ar
ativ
e
r
esu
lts
o
f
t
h
e
th
r
ee
-
co
m
p
o
n
en
t
an
a
l
y
s
is
.
T
h
e
P
C
A
p
r
o
d
u
cin
g
s
ec
o
n
d
o
v
er
al
l
r
ec
o
g
n
itio
n
ac
c
u
r
ac
y
w
it
h
8
9
.
8
%
af
ter
KP
C
A
.
I
n
t
h
e
f
ir
s
t,
t
est
d
ataset
o
f
1
0
%,
I
C
A
r
esu
l
tin
g
t
h
e
w
o
r
s
t r
ec
o
g
n
itio
n
ac
c
u
r
ac
y
a
n
d
y
ield
i
n
g
o
v
er
all
3
4
% r
ec
o
g
n
itio
n
o
n
l
y
.
5.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
SCO
P
E
I
n
t
h
e
s
t
u
d
y
,
P
C
A
,
KP
C
A
,
a
n
d
I
C
A
u
s
ed
f
o
r
d
ata
r
ed
u
ct
i
o
n
an
d
r
ec
o
g
n
itio
n
o
f
to
m
ato
late
b
li
g
h
t
f
r
o
m
a
h
ea
lt
h
y
lea
f
i
n
d
i
g
ital
i
m
a
g
es.
T
h
e
p
lan
t
d
is
ea
s
e
r
ec
o
g
n
itio
n
a
n
d
clas
s
i
f
icatio
n
b
ased
o
n
t
w
o
cla
s
s
e
s
i.e
.
i
m
a
g
es
a
f
f
ec
ted
by
lat
e
b
lig
h
t
a
n
d
h
ea
l
th
y
.
Fo
r
f
ea
t
u
r
e
ex
tr
ac
tio
n
,
w
e
u
s
ed
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
atio
n
b
ased
o
n
Haar
w
a
v
elet.
As
th
e
r
e
s
u
l
ts
s
h
o
w
,
ex
tr
ac
ted
f
ea
tu
r
e
d
ata
f
r
o
m
t
h
e
i
m
a
g
es
o
f
p
lan
t
d
is
ea
s
es
b
y
r
ed
u
ci
n
g
d
i
m
e
n
s
i
o
n
s
co
u
ld
r
ed
u
ce
th
e
r
u
n
n
in
g
ti
m
e
f
o
r
class
i
f
icatio
n
,
a
n
d
ac
ce
p
tab
le
r
ec
o
g
n
itio
n
r
esu
lt
s
co
u
ld
o
b
ta
in
.
T
h
e
m
e
t
h
o
d
u
s
ed
in
th
e
s
t
u
d
y
p
r
o
v
ed
th
at
P
C
A
,
KP
C
A
,
a
n
d
I
C
A
u
s
ed
f
o
r
ex
tr
ac
ted
f
ea
t
u
r
e
d
ata
r
ed
u
ctio
n
f
r
o
m
t
o
m
a
to
late
b
lig
h
t
i
m
a
g
es
a
n
d
also
u
s
ed
f
o
r
r
ec
o
g
n
itio
n
p
u
r
p
o
s
e.
Fin
all
y
,
t
h
e
d
is
tan
ce
b
et
w
ee
n
tr
ai
n
i
n
g
an
d
test
i
n
g
d
ataset
co
m
p
u
ted
b
y
E
u
clid
ea
n
d
i
s
ta
n
ce
m
ea
s
u
r
e
by
t
w
o
cla
s
s
e
s
.
I
f
th
e
d
is
tan
ce
i
s
le
s
s
f
r
o
m
a
n
y
o
f
t
w
o
cla
s
s
es,
th
e
n
t
h
e
p
ar
tic
u
la
r
test
i
n
g
i
m
a
g
e
b
elo
n
g
s
to
o
n
e
o
f
t
h
e
t
w
o
cla
s
s
es
eith
er
late
b
lig
h
t
i
n
f
ec
ted
o
r
h
ea
lth
y
o
n
e.
T
h
e
P
C
A
is
a
k
in
d
o
f
lin
ea
r
p
r
o
jectio
n
,
an
d
it
co
u
ld
n
o
t
co
r
r
ec
tl
y
h
an
d
le
n
o
n
-
li
n
ea
r
d
ata,
b
u
t
it
co
u
ld
h
a
n
d
le
u
s
i
n
g
KP
C
A
.
A
ll
t
h
e
p
r
o
p
er
ties
o
f
o
r
d
in
ar
y
P
C
A
ca
r
r
y
o
v
er
to
KP
C
A
.
T
h
e
Ker
n
al
P
C
A
i
s
li
n
ea
r
in
f
ea
tu
r
e
s
p
ac
e
an
d
n
o
n
li
n
ea
r
in
th
e
in
p
u
t
s
p
ac
e.
As
s
u
ch
,
it
ca
n
b
e
ap
p
lied
to
all
th
o
s
e
d
o
m
ain
s
w
h
er
e
o
r
d
in
ar
y
P
C
A
u
s
ed
f
o
r
f
ea
t
u
r
e
ex
tr
ac
tio
n
o
r
d
ata
r
ed
u
ctio
n
,
f
o
r
w
h
ich
n
o
n
l
in
ea
r
ex
ten
s
io
n
w
o
u
ld
m
ak
e
s
en
s
e.
As
co
m
p
ar
e
to
P
C
A
,
KP
C
A
a
n
d
I
C
A
is
n
o
t
o
n
l
y
s
tatis
t
ical
in
d
ep
en
d
en
t
u
p
to
s
ec
o
n
d
o
r
d
er
b
u
t
u
p
to
all
t
h
e
in
d
iv
id
u
al
co
m
p
o
n
en
ts
o
f
t
h
e
o
u
tp
u
t
v
ec
to
r
an
d
i
n
v
o
l
v
e
s
n
o
co
n
s
tr
ai
n
t
o
f
o
r
th
o
g
o
n
alit
y
.
T
ab
le
1
.
R
ec
o
g
n
itio
n
a
n
d
C
las
s
if
ica
tio
n
o
f
T
o
m
ato
L
ate
B
li
g
h
t a
cc
u
r
ac
y
T
e
st
i
n
g
D
a
t
a
se
t
P
r
i
n
c
i
p
a
l
C
o
m
p
o
n
e
n
t
A
n
a
l
y
si
s
K
e
r
n
e
l
P
r
i
n
c
i
p
a
l
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o
m
p
o
n
e
n
t
A
n
a
l
y
si
s
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n
d
e
p
e
n
d
e
n
t
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o
m
p
o
n
e
n
t
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n
a
l
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si
s
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a
t
e
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l
i
g
h
t
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n
f
e
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t
e
d
H
e
a
l
t
h
y
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a
t
e
B
l
i
g
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t
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n
f
e
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t
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d
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e
a
l
t
h
y
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a
t
e
B
l
i
g
h
t
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n
f
e
c
t
e
d
H
e
a
l
t
h
y
1
0
%
1
0
0
%
1
0
0
%
1
0
0
%
1
0
0
%
0%
1
0
0
%
2
0
%
9
0
%
9
2
%
9
0
%
9
2
%
7
5
%
2
5
%
3
0
%
7
9
%
8
8
%
9
3
%
4
7
%
7%
1
0
0
%
4
0
%
8
4
%
7
8
%
8
9
%
4
8
%
4
7
%
6
5
%
5
0
%
9
6
%
2
1
%
1
0
0
%
1
0
%
4
2
%
5
5
%
O
v
e
r
a
l
l
A
c
c
u
r
a
c
y
8
9
.
8
%
7
5
.
8
%
9
6
.
4
%
5
1
.
1
2
%
3
4
%
6
9
%
ACK
NO
WL
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.
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Dep
ar
t
m
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Ag
r
icu
ltu
r
e
Scien
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s
,
D
AV
Un
i
v
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it
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J
alan
d
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ar
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P
u
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ab
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ia.
,
f
o
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ld
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W
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a
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Mr
.
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o
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an
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a,
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s
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ess
o
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ar
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o
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ter
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n
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T
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h
ilai,
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h
atti
s
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ar
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n
d
ia,
f
o
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ta
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t
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u
s
ed
in
t
h
e
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
Ke
l
m
a
m
,
“
P
lan
t
Dise
a
se
s E
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c
y
c
lo
p
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d
ia
,
”
Bri
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n
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ica
,
2
0
1
2
.
[2
]
C.
C.
T
u
c
k
e
r
a
n
d
S
.
Ch
a
k
ra
b
o
rty
,
“
Qu
a
n
ti
tativ
e
a
ss
e
ss
m
e
n
t
o
f
les
io
n
c
h
a
ra
c
teristics
a
n
d
d
ise
a
se
se
v
e
rit
y
u
sin
g
d
ig
it
a
l
im
a
g
e
p
ro
c
e
ss
in
g
,”
J
o
u
rn
a
l
Ph
y
to
p
a
th
o
lo
g
y
,
v
o
l.
1
4
5
,
p
p
.
2
7
3
–
2
7
8
,
1
9
9
7
.
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199
[3
]
I.
S
.
A
h
m
a
n
d
,
et
a
l.
,
“
Co
lo
r
c
las
s
if
ier
f
o
r
s
y
m
p
to
m
a
ti
c
so
y
b
e
a
n
se
e
d
s
u
sin
g
i
m
a
g
e
p
ro
c
e
ss
in
g
,”
Pl
a
n
t
Dise
a
se
,
v
o
l.
83
,
p
p
.
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–
3
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,
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9
9
9
.
[4
]
J.
C.
L
a
i,
e
t
a
l
.
,
“
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d
v
a
n
c
e
s
in
re
se
a
rc
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o
n
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o
m
p
u
ter
-
v
isio
n
d
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n
o
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o
f
c
ro
p
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ise
a
se
s
,”
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c
ien
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a
Ag
ri
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u
lt
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ra
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in
ica
,
v
o
l.
42
,
p
p
.
1
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5
–
1
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2
,
2
0
0
9
.
[5
]
C.
H.
Bo
c
k
,
e
t
a
l
.
,
“
A
u
to
m
a
ted
i
m
a
g
e
a
n
a
l
y
sis
o
f
th
e
se
v
e
rit
y
o
f
fo
li
a
r
c
it
ru
s ca
n
k
e
r
s
y
m
p
to
m
s
,”
Pl
a
n
t
Dise
a
se
,
v
o
l.
93
,
p
p
.
6
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0
–
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9
.
[6
]
M
.
Ra
m
a
k
rish
n
a
n
a
n
d
A
.
S
.
A
.
Nish
a
,
“
G
ro
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n
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n
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t
L
e
a
f
Dis
e
a
se
De
tec
ti
o
n
a
n
d
Clas
sif
ica
ti
o
n
b
y
u
sin
g
Ba
c
k
P
r
o
p
a
g
a
ti
o
n
Ne
tw
o
rk
,”
Pro
c
.
IEE
E
In
t.
Co
n
f
.
o
n
Co
mm
u
n
ica
ti
o
n
s
a
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
,
M
e
lma
ru
v
a
th
u
r
,
p
p
.
9
6
4
-
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,
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0
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5
.
[7
]
N.
M
.
T
a
h
ir,
e
t
a
l.
,
“
Clas
sif
i
c
a
ti
o
n
o
f
El
a
e
is
G
u
in
e
e
n
sis
Dise
a
se
-
L
e
a
f
u
n
d
e
r
u
n
c
o
n
tr
o
ll
e
d
il
l
u
m
in
a
ti
o
n
u
sin
g
RBF
Ne
tw
o
rk
,”
Pro
c
.
IEE
E
In
t.
C
o
n
f.
o
n
C
o
n
tr
o
l
S
y
ste
m,
Co
mp
u
ti
n
g
,
a
n
d
E
n
g
in
e
e
rin
g
,
M
a
l
a
y
sia
,
p
p
.
6
1
7
–
6
2
1
,
2
0
1
4
.
[8
]
B.
L
i,
e
t
a
l
.
,
“
H
y
p
e
rsp
e
c
tral
id
e
n
ti
f
ica
ti
o
n
o
f
rice
d
ise
a
se
s
a
n
d
p
e
sts
b
a
se
d
o
n
p
rin
c
i
p
a
l
c
o
m
p
o
n
e
n
t
a
n
a
ly
sis
a
n
d
p
ro
b
a
b
il
isti
c
n
e
u
ra
l
n
e
tw
o
rk
,
”
T
ra
n
sa
c
ti
o
n
s
o
f
t
h
e
CS
AE
,
v
o
l
.
25
,
p
p
.
1
4
3
–
1
4
7
,
2
0
0
9
.
[9
]
H
.
Jie
,
“
A
p
p
li
c
a
ti
o
n
o
f
P
CA
M
e
th
o
d
o
n
P
e
st
In
f
o
rm
a
ti
o
n
De
tec
ti
o
n
o
f
El
e
c
tro
n
ic
No
se
,”
i
n
th
e
p
ro
c
e
e
d
in
g
o
f
IEE
E
in
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
I
n
fo
r
ma
ti
o
n
A
q
u
siti
o
n
.
W
e
ih
a
i,
S
h
a
n
d
o
n
g
,
C
h
i
n
a
,
p
p
.
1
4
5
6
-
1
4
6
8
,
2
0
0
6
.
[1
0
]
M
.
M
.
S
e
in
l
,
e
t
a
l
.
,
“
A
u
th
e
n
ti
c
a
t
io
n
s
o
f
M
y
a
n
m
a
r
Na
ti
o
n
a
l
Re
g
is
tratio
n
Ca
rd
,”
In
d
o
n
e
si
a
n
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
I
n
fo
rm
a
ti
c
s (
IJ
EE
I),
v
o
l.
2
,
p
p
.
53
-
58
,
2
0
1
3
.
[1
1
]
M.
L
e
n
n
o
n
,
et
al
.,
“
In
d
e
p
e
n
d
e
n
t
Co
m
p
o
n
e
n
t
A
n
a
ly
sis
as
a
to
o
l
f
o
r
th
e
d
im
e
n
sio
n
a
li
ty
re
d
u
c
ti
o
n
a
n
d
t
h
e
re
p
re
se
n
tatio
n
of
h
y
p
e
rsp
e
c
tral
i
m
a
g
e
s.
G
e
o
sc
ien
c
e
a
n
d
Re
m
o
te
S
e
n
sin
g
S
y
m
p
o
siu
m
,
”
IEE
E
2
0
0
1
In
ter
n
a
ti
o
n
a
l
,
v
o
l.
6
,
p
p
.
2
8
9
3
-
2
8
9
,
2
0
0
1
.
[1
2
]
Y.
F
a
n
g
z
h
o
u
,
et
al
.
,
“
I
n
d
e
p
e
n
d
e
n
t
P
r
in
c
ip
a
l
C
o
m
p
o
n
e
n
t
A
n
a
ly
sis
fo
r
b
i
o
lo
g
ica
ll
y
m
e
a
n
in
g
f
u
l
d
im
e
n
sio
n
re
d
u
c
ti
o
n
of
larg
e
b
io
lo
g
ica
l
d
a
ta
se
ts
,”
BM
C
Bi
o
i
n
fo
rm
a
ti
c
s
,
p
p
.
13
-
24
,
2
0
1
2
.
[1
3
]
B.
S
c
h
ö
lk
o
p
f
,
e
t
a
l
.
,
“
Ke
rn
e
l
p
r
i
n
c
ip
a
l
c
o
m
p
o
n
e
n
t
a
n
a
ly
sis
,”
Arti
fi
c
ia
l
Ne
u
ra
l
Ne
tw
o
rk
s
ICANN'9
7
,
W
.
Ge
rs
tn
e
r
a
n
d
A.
Ge
rm
o
n
d
a
n
d
M
.
H
a
sle
r
a
n
d
J
.
-
D.
Nico
u
d
(
Ed
s.),
S
p
rin
g
e
r
L
e
c
tu
re
No
tes
i
n
C
o
mp
u
ter
S
c
i
e
n
c
e
,
v
o
l.
1
3
2
7
,
p
p
.
5
8
3
-
588
,
1
9
9
7
.
[1
4
]
X
.
L
i,
e
t
a
l
.
,
“
De
term
in
a
ti
o
n
o
f
d
ry
m
a
tt
e
r
c
o
n
ten
t
o
f
tea
b
y
n
e
a
r
a
n
d
m
id
d
le
i
n
f
ra
re
d
sp
e
c
tro
sc
o
p
y
c
o
u
p
led
w
it
h
w
a
v
e
let
-
b
a
se
d
d
a
ta
m
in
in
g
a
lg
o
rit
h
m
,”
Co
mp
u
ter
a
n
d
El
e
c
tro
n
ics
i
n
Ag
ric
u
lt
u
re
,
v
o
l.
98
,
p
p
.
26
-
53
,
2
0
1
3
.
[1
5
]
R.
P
o
l
ik
a
r
,
“
T
h
e
W
a
v
e
let
T
u
to
rial
P
a
rt
I
,”
h
tt
p
:
//
u
se
rs.ro
w
a
n
.
e
d
u
/
~
p
o
li
k
a
r/W
A
V
EL
ET
S
/
W
T
p
a
rt1
.
h
tm
l
.
[1
6
]
O.
S
.
Ja
h
ro
m
i,
e
t
a
l
.
,
“
A
lg
e
b
ra
ic
th
e
o
ry
o
f
o
p
ti
m
a
l
f
il
ter
b
a
n
k
s
,”
I
EE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
i
g
n
a
l
Pr
o
c
e
ss
in
g
,
v
o
l.
51
,
pp.
4
4
2
–
4
5
7
,
2
0
0
3
.
[1
7
]
C.
R
.
G
o
n
z
á
lez
a
n
d
R
.
E
.
W
o
o
d
s
,
“
Dig
it
a
l
Im
a
g
e
P
r
o
c
e
ss
in
g
,”
PHI
, 3
rd
Ed
,
2
0
0
8
.
[1
8
]
A
.
A
k
h
tar,
e
t
a
l
.
,
“
A
u
to
m
a
ted
P
lan
t
Dise
a
se
A
n
a
l
y
sis
(
A
P
DA
):
P
e
rf
o
rm
a
n
c
e
Co
m
p
a
riso
n
o
f
M
a
c
h
in
e
L
e
a
rn
in
g
T
e
c
h
n
iq
u
e
s
”
,
FIT
,
Fro
n
ti
e
rs
o
f
I
n
fo
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
,
Fro
n
ti
e
rs
o
f
In
fo
rm
a
t
io
n
T
e
c
h
n
o
l
o
g
y
,
p
p
.
60
-
65
,
2
0
1
3
.
[1
9
]
N
.
G
h
a
ff
a
rz
a
d
e
h
,
“
A
Ne
w
M
e
t
h
o
d
f
o
r
Re
c
o
g
n
it
io
n
o
f
A
rc
in
g
F
a
u
lt
s
in
T
ra
n
s
m
issio
n
L
in
e
s
u
sin
g
W
a
v
e
let
T
ra
n
s
f
o
r
m
a
n
d
Co
rre
latio
n
Co
e
ff
icie
n
t
,”
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
In
f
o
r
ma
ti
c
s
(
I
J
EE
I)
,
v
o
l/
issu
e
:
1
(
1
)
,
p
p
.
1
-
7
,
2
0
1
3
.
[2
0
]
J.
F
.
Ca
rd
a
so
,
“
De
p
e
n
d
e
n
c
e
,
c
o
rr
e
latio
n
a
n
d
G
a
u
ss
ian
it
y
in
in
d
e
p
e
n
d
e
n
t
c
o
m
p
o
n
e
n
t
a
n
a
ly
sis
,”
J
o
u
rn
a
l
o
f
M
a
c
h
in
e
L
e
a
rn
in
g
Res
e
a
rc
h
,
v
o
l.
4
,
p
p
.
1
1
7
7
-
1
2
0
3
,
2
0
0
3
.
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