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en
ts
.
I
n
co
n
tr
ast
to
tr
ad
itio
n
al
d
atasets
th
at
co
m
e
f
r
o
m
c
o
n
tr
o
lled
en
v
i
r
o
n
m
e
n
ts
,
th
is
o
n
e
is
d
e
v
elo
p
e
d
b
y
d
ir
ec
t
in
ter
ac
tio
n
with
f
ar
m
er
s
wh
o
h
av
e
p
r
icele
s
s
ag
r
icu
ltu
r
al
e
x
p
er
tis
e.
W
e
en
s
u
r
ed
th
e
a
u
th
en
ticity
a
n
d
r
ele
v
an
ce
o
f
o
u
r
d
ataset
b
y
wo
r
k
i
n
g
clo
s
ely
with
f
ar
m
er
s
,
im
m
er
s
in
g
o
u
r
s
elv
es
in
th
e
f
ield
s
,
an
d
d
i
r
ec
t
o
b
s
er
v
atio
n
o
f
illn
ess
s
y
m
p
to
m
s
.
T
h
is
s
tr
en
g
th
en
ed
t
h
e
ef
f
ec
tiv
en
ess
an
d
r
elev
an
ce
o
f
o
u
r
s
tu
d
y
f
in
d
in
g
s
.
O
u
r
c
o
l
l
e
c
t
i
o
n
c
o
n
t
a
i
n
s
a
w
i
d
e
r
a
n
g
e
o
f
a
g
r
i
c
u
l
t
u
r
a
l
s
p
e
c
i
m
e
n
s
,
i
n
c
l
u
d
i
n
g
l
e
a
v
e
s
f
r
o
m
s
e
v
e
r
a
l
c
r
o
p
s
,
i
n
c
l
u
d
i
n
g
t
o
m
a
t
o
,
s
o
y
b
e
a
n
,
m
a
n
g
o
,
j
a
v
a
p
l
u
m
,
b
a
y
l
e
a
f
,
a
n
d
l
a
d
y
f
i
n
g
e
r
s
.
E
q
u
i
p
p
e
d
w
it
h
t
h
i
s
a
b
u
n
d
a
n
t
a
n
d
v
a
r
i
e
d
d
a
t
a
s
et
,
w
e
l
e
v
e
r
a
g
e
t
h
e
c
a
p
a
b
i
l
it
i
es
o
f
s
o
p
h
i
s
t
i
c
at
e
d
a
l
g
o
r
i
t
h
m
i
c
a
p
p
r
o
a
c
h
es
,
s
u
c
h
a
s
s
u
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
s
(
S
V
M
)
,
r
a
n
d
o
m
f
o
r
e
s
t
(
R
F
)
,
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
(
C
N
N
)
,
e
x
t
r
e
m
e
g
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
(X
G
B
o
o
s
t
)
a
n
d
l
o
g
i
s
ti
c
r
e
g
r
e
s
s
i
o
n
(
L
R
)
.
E
v
e
r
y
m
e
t
h
o
d
h
a
s
i
t
s
o
w
n
a
d
v
a
n
t
a
g
e
s
.
F
o
r
e
x
a
m
p
l
e
,
RF
is
g
o
o
d
w
i
t
h
h
i
g
h
-
d
i
m
e
n
s
i
o
n
a
l
d
a
t
a
,
C
N
N
is
g
o
o
d
a
t
e
x
t
r
a
c
t
i
n
g
s
p
a
t
ia
l
f
ea
t
u
r
e
s
,
SV
M
is
g
o
o
d
a
t
b
i
n
a
r
y
c
l
a
s
s
i
f
i
c
at
i
o
n
,
a
n
d
X
G
B
o
o
s
t
h
as
b
e
tt
e
r
p
r
e
d
i
c
ti
o
n
a
c
c
u
r
a
c
y
.
T
h
e
p
r
i
m
a
r
y
o
b
j
e
ct
iv
e
o
f
t
h
i
s
r
es
e
a
r
c
h
i
s
t
o
i
d
e
n
t
i
f
y
t
h
e
m
o
s
t
e
f
f
ec
t
i
v
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
f
o
r
a
c
c
u
r
a
t
e
d
i
s
e
a
s
e
d
et
e
c
ti
o
n
ac
r
o
s
s
d
i
v
e
r
s
e
a
g
r
i
c
u
lt
u
r
a
l
c
o
n
t
ex
t
s
i
n
I
n
d
i
a
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
Rela
t
ed
w
o
rk
M
a
c
h
i
n
e
l
ea
r
n
i
n
g
t
e
c
h
n
i
q
u
es
ar
e
w
i
d
e
l
y
u
s
e
d
t
o
i
d
e
n
t
i
f
y
p
at
te
r
n
s
i
n
c
o
m
p
l
e
x
d
a
t
as
e
ts
,
p
a
r
t
ic
u
l
a
r
l
y
f
o
r
p
l
a
n
t
d
i
s
e
a
s
e
d
e
t
e
ct
i
o
n
.
I
n
i
ti
a
l
s
t
e
p
s
t
y
p
ic
a
l
l
y
i
n
v
o
l
v
e
c
r
e
at
in
g
a
d
a
t
a
b
a
s
e
u
s
i
n
g
p
u
b
l
i
c
l
y
a
v
a
i
l
a
b
l
e
d
a
t
as
e
ts
o
r
i
m
a
g
i
n
g
e
q
u
i
p
m
e
n
t
.
P
r
e
p
r
o
c
ess
i
n
g
t
e
c
h
n
i
q
u
es
s
u
c
h
as
i
m
a
g
e
s
c
a
li
n
g
,
n
o
i
s
e
r
e
d
u
c
ti
o
n
,
c
o
n
t
r
a
s
t
e
n
h
a
n
c
e
m
e
n
t
,
a
n
d
c
o
l
o
r
s
p
a
c
e
c
o
n
v
e
r
s
i
o
n
a
r
e
c
r
u
c
i
a
l
f
o
r
m
a
x
i
m
i
zi
n
g
i
m
a
g
e
q
u
a
l
i
t
y
a
n
d
s
h
o
r
t
e
n
i
n
g
p
r
o
c
e
s
s
in
g
t
i
m
e
s
.
R
ec
en
t
ad
v
an
ce
m
en
ts
in
m
ac
h
in
e
lear
n
in
g
h
a
v
e
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
in
d
etec
tin
g
p
lan
t
d
is
ea
s
es.
Fo
r
in
s
tan
ce
,
Priy
a
r
ad
h
ik
a
d
e
v
i
et
a
l.
[
1
1
]
s
u
cc
ess
f
u
lly
u
tili
ze
d
im
ag
e
p
r
o
ce
s
s
in
g
tec
h
n
iq
u
es
to
en
h
a
n
ce
d
is
ea
s
e
d
etec
tio
n
ca
p
ab
ilit
ies,
s
ig
n
if
ican
tly
r
ed
u
cin
g
ag
r
icu
ltu
r
al
lo
s
s
es
th
r
o
u
g
h
ef
f
ec
tiv
e
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
s
eg
m
en
tatio
n
.
Aza
d
b
ak
h
t
et
a
l.
[
1
2
]
u
s
ed
m
ac
h
in
e
le
ar
n
in
g
to
class
if
y
wh
ea
t
d
is
ea
s
es
u
s
in
g
v
ar
io
u
s
alg
o
r
ith
m
s
,
in
clu
d
in
g
SVM
, k
-
Neig
h
b
o
r
s
class
if
ier
,
d
ec
is
io
n
f
o
r
ests
,
lin
ea
r
d
is
cr
im
in
an
t a
n
aly
s
is
,
n
aiv
e
B
ay
es
class
if
ier
,
C
NN
,
an
d
r
ec
u
r
r
e
n
t
n
eu
r
al
n
etwo
r
k
s
.
W
ah
al
e
t
a
l
.
[
1
3
]
e
x
ten
d
e
d
th
e
u
s
e
o
f
ML
b
y
n
o
t
o
n
ly
d
etec
tin
g
leaf
d
is
ea
s
es
b
u
t
a
ls
o
p
r
ed
ictin
g
cr
o
p
o
u
tco
m
e
s
,
illu
s
tr
atin
g
th
e
b
r
o
a
d
er
ap
p
licatio
n
s
o
f
th
ese
tech
n
o
lo
g
ies
in
ag
r
icu
ltu
r
al
m
an
ag
em
en
t.
J
h
a
et
a
l.
[
1
4
]
co
n
d
u
cted
a
co
m
p
r
e
h
en
s
iv
e
co
m
p
ar
ativ
e
an
aly
s
is
o
f
v
ar
io
u
s
ML
alg
o
r
ith
m
s
f
o
r
p
lan
t
d
is
ea
s
e
d
etec
tio
n
,
h
ig
h
li
g
h
tin
g
th
e
v
er
s
atility
an
d
e
f
f
ec
tiv
en
ess
o
f
th
ese
m
o
d
els
ac
r
o
s
s
d
if
f
er
en
t
cr
o
p
s
.
T
h
eir
f
in
d
i
n
g
s
u
n
d
er
s
co
r
e
t
h
e
p
o
ten
tial
o
f
ML
to
r
ev
o
l
u
tio
n
ize
ag
r
icu
ltu
r
al
p
r
ac
tices b
y
en
a
b
lin
g
ac
cu
r
ate
d
is
ea
s
e
id
en
tific
atio
n
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
cr
o
p
s
.
C
NN
is
o
n
e
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
t
h
at
is
wid
ely
u
s
ed
in
p
lan
t
d
is
ea
s
e
d
etec
tio
n
d
u
e
to
its
p
o
wer
f
u
l
ab
ilit
y
to
au
to
m
atica
lly
ex
tr
ac
t
a
n
d
lear
n
f
ea
tu
r
e
s
f
r
o
m
im
ag
es.
Sil
v
a
et
a
l.
[
1
5
]
u
s
ed
C
NNs
in
ag
r
icu
ltu
r
al
a
p
p
licatio
n
s
,
p
ar
t
icu
lar
ly
f
o
r
esti
m
atin
g
leaf
d
am
ag
e
in
s
o
y
b
ea
n
leav
es
an
d
also
ex
p
lo
r
ed
th
e
ef
f
ec
tiv
en
ess
o
f
s
y
n
th
etic
im
a
g
es
to
tr
ain
C
NN
m
o
d
els,
o
f
f
er
in
g
a
n
o
v
el
a
p
p
r
o
ac
h
f
o
r
h
a
n
d
lin
g
v
ar
iatio
n
s
in
r
ea
l
-
wo
r
ld
d
ata.
T
m
et
a
l.
[
1
6
]
u
tili
ze
d
th
e
L
eNe
t
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
m
o
d
el
to
d
etec
t
an
d
d
iag
n
o
s
e
illn
ess
es
in
to
m
ato
p
lan
ts
,
ac
h
iev
in
g
an
a
v
er
ag
e
ac
cu
r
ac
y
o
f
9
4
-
9
5
%,
d
em
o
n
s
tr
atin
g
th
e
f
ea
s
ib
ilit
y
o
f
th
e
n
eu
r
al
n
etwo
r
k
tech
n
i
q
u
e
ev
en
in
ch
allen
g
i
n
g
s
itu
atio
n
s
.
Sar
d
o
g
an
et
a
l.
[
1
7
]
d
ev
el
o
p
ed
a
m
et
h
o
d
f
o
r
au
to
m
atic
d
etec
tio
n
an
d
ca
te
g
o
r
izatio
n
o
f
d
is
o
r
d
er
s
u
s
in
g
a
c
o
m
b
in
atio
n
o
f
C
NN
an
d
lear
n
in
g
v
ec
to
r
q
u
an
tizatio
n
(
L
VQ)
alg
o
r
ith
m
s
.
Ag
ar
wal
et
a
l.
[
1
8
]
d
ev
elo
p
ed
an
ef
f
icien
t
C
NN
m
o
d
el
s
p
ec
if
ically
f
o
r
to
m
ato
cr
o
p
d
is
ea
s
e
id
en
tific
atio
n
,
f
o
cu
s
in
g
o
n
o
p
tim
izin
g
C
NN
ar
ch
itectu
r
es
to
en
h
a
n
ce
p
er
f
o
r
m
an
ce
i
n
id
en
tify
in
g
v
a
r
io
u
s
d
is
ea
s
es
a
f
f
ec
tin
g
to
m
ato
cr
o
p
s
.
C
NNs
ar
e
p
ar
ticu
lar
ly
ef
f
ec
tiv
e
in
an
aly
zin
g
co
m
p
lex
v
is
u
al
d
ata,
s
u
ch
as im
ag
es o
f
leav
es,
b
y
id
en
tif
y
in
g
p
atter
n
s
an
d
f
ea
tu
r
es a
s
s
o
ciate
d
with
s
p
ec
if
ic
d
is
ea
s
es.
B
ey
o
n
d
tr
ad
itio
n
al
C
NNs,
v
ar
io
u
s
d
ee
p
lear
n
i
n
g
(
DL
)
m
eth
o
d
s
,
s
u
ch
as
f
in
e
-
tu
n
in
g
,
tr
an
s
f
er
lear
n
in
g
,
an
d
h
y
b
r
i
d
m
o
d
els,
h
av
e
b
ee
n
ef
f
ec
tiv
ely
ap
p
lied
to
d
etec
t
p
lan
t
leaf
d
is
ea
s
es.
T
h
ese
m
eth
o
d
s
an
aly
ze
im
ag
es
f
r
o
m
d
if
f
er
en
t
p
h
ases
an
d
class
if
y
th
em
in
to
p
r
ed
ef
in
e
d
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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&
C
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p
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,
Vo
l.
15
,
No
.
2
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Ap
r
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20
25
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2084
2
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1
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Da
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L
ea
f
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et
T
h
is
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tu
d
y
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tili
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d
th
e
L
ea
f
s
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d
ataset,
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cu
r
ated
c
o
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n
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f
h
i
g
h
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r
eso
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tio
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t
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ap
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y
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p
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el
u
s
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m
o
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ile
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ices
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n
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er
d
i
v
er
s
e
lig
h
tin
g
co
n
d
itio
n
s
,
th
e
d
ataset
in
clu
d
es
im
ag
es
o
f
f
u
n
g
al,
b
ac
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d
v
ir
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ec
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ac
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h
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to
m
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7
,
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7
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m
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ay
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im
ag
es.
T
h
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ataset
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s
p
lit
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8
0
%
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o
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tr
ain
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g
an
d
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%
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e
n
s
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ate
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lear
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m
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o
r
p
la
n
t
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ea
s
e
d
etec
tio
n
.
Fig
u
r
e
2
d
is
p
lay
s
s
ix
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d
o
m
ly
s
elec
ted
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ai
r
s
o
f
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d
is
ea
s
e
co
m
b
in
atio
n
s
f
r
o
m
th
e
d
ataset
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Fig
u
r
e
2
.
Sam
p
le
lea
f
im
ag
es
2
.
2
.
2
.
I
m
a
g
e
p
re
pro
ce
s
s
ing
T
o
en
s
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th
e
q
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ality
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d
co
n
s
is
ten
cy
o
f
th
e
ca
p
tu
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ed
im
ag
es,
s
ev
er
al
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r
ep
r
o
ce
s
s
in
g
tech
n
i
q
u
es we
r
e
ap
p
lied
u
n
if
o
r
m
ly
ac
r
o
s
s
th
e
d
ataset.
I
m
ag
es
wer
e
r
esize
d
to
a
s
tan
d
ar
d
r
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lu
tio
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o
f
2
5
6
×
2
5
6
p
ix
els,
en
s
u
r
in
g
co
m
p
atib
ilit
y
with
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
N
o
r
m
aliza
tio
n
was
p
er
f
o
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m
ed
to
s
tan
d
ar
d
ize
p
ix
el
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ten
s
ity
v
alu
es,
m
in
im
izin
g
th
e
im
p
ac
t
o
f
lig
h
tin
g
v
ar
ia
tio
n
s
an
d
ac
ce
ler
atin
g
m
o
d
el
tr
ain
in
g
.
C
o
n
t
r
ast
ad
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s
tm
en
ts
wer
e
m
ad
e
to
e
n
h
an
ce
th
e
v
is
ib
ilit
y
o
f
d
is
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s
e
s
y
m
p
to
m
s
,
im
p
r
o
v
in
g
t
h
e
m
o
d
els'
ab
ilit
y
to
d
if
f
er
en
tiate
b
etwe
en
h
ea
lth
y
an
d
d
is
ea
s
ed
ar
ea
s
.
Gau
s
s
ian
n
o
is
e
r
ed
u
ctio
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was
ap
p
lied
to
r
em
o
v
e
en
v
ir
o
n
m
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tal
a
n
d
d
ev
ice
-
r
ela
ted
n
o
is
e
wh
ile
p
r
eser
v
in
g
im
p
o
r
tan
t
im
ag
e
f
ea
tu
r
es.
T
h
ese
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
wer
e
cr
itical
f
o
r
im
p
r
o
v
in
g
d
a
ta
f
id
elity
an
d
en
s
u
r
in
g
th
at
th
e
d
ataset
was
o
p
tim
ally
p
r
e
p
a
r
ed
f
o
r
s
u
b
s
eq
u
e
n
t
an
aly
s
is
,
lay
in
g
a
s
tr
o
n
g
f
o
u
n
d
atio
n
f
o
r
ac
cu
r
ate
m
ac
h
in
e
lea
r
n
in
g
m
o
d
el
p
e
r
f
o
r
m
an
ce
.
2
.
2
.
3
.
I
m
a
g
e
a
ug
m
ent
a
t
io
n
I
m
ag
e
au
g
m
e
n
tatio
n
was
em
p
lo
y
ed
to
en
h
an
ce
d
ataset
d
iv
er
s
ity
an
d
im
p
r
o
v
e
th
e
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
b
y
g
en
e
r
atin
g
s
y
n
th
etic
v
ar
iatio
n
s
in
th
e
tr
ain
i
n
g
d
ata.
T
ec
h
n
i
q
u
es
s
u
ch
as r
o
tatio
n
,
f
lip
p
in
g
,
s
ca
lin
g
,
cr
o
p
p
in
g
,
an
d
b
r
i
g
h
tn
ess
ad
ju
s
tm
en
t w
er
e
ap
p
lied
t
o
in
tr
o
d
u
ce
v
a
r
iatio
n
s
in
s
p
atial
attr
ib
u
tes
an
d
o
b
ject
co
n
f
ig
u
r
atio
n
s
,
p
r
e
v
en
tin
g
o
v
er
f
itti
n
g
an
d
im
p
r
o
v
in
g
m
o
d
el
r
o
b
u
s
tn
ess
.
Fin
e
-
tu
n
ed
ad
ju
s
tm
en
ts
lik
e
co
lo
r
v
ar
iatio
n
s
,
lu
m
in
an
ce
ch
a
n
g
es
,
an
d
n
o
is
e
ad
d
itio
n
s
im
u
late
r
ea
l
-
wo
r
ld
lig
h
tin
g
an
d
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
.
R
ig
o
r
o
u
s
ly
im
p
lem
en
tin
g
th
e
s
e
tech
n
iq
u
es
en
r
ich
es
d
ataset
d
iv
er
s
ity
,
en
ab
lin
g
m
o
d
els
to
lear
n
f
r
o
m
a
wid
er
r
an
g
e
o
f
s
ce
n
ar
io
s
.
T
h
er
e
f
o
r
e,
im
ag
e
au
g
m
e
n
tatio
n
is
v
ital
f
o
r
en
h
a
n
cin
g
th
e
ef
f
ec
tiv
en
ess
an
d
ad
a
p
tab
ilit
y
o
f
co
m
p
u
ter
v
is
io
n
s
y
s
tem
s
ac
r
o
s
s
v
ar
io
u
s
ap
p
licatio
n
s
.
2
.
2
.
4
.
F
ea
t
ure
e
x
t
ra
ct
i
o
n
I
n
th
e
f
ea
tu
r
e
ex
tr
ac
ti
o
n
p
r
o
ce
s
s
,
k
ey
ch
a
r
ac
ter
is
tics
s
u
ch
as tex
tu
r
e,
c
o
lo
r
,
an
d
s
h
ap
e
wer
e
id
en
tifie
d
an
d
ex
tr
ac
ted
f
r
o
m
th
e
leaf
im
ag
es
to
p
r
o
v
id
e
m
ea
n
in
g
f
u
l
in
p
u
ts
f
o
r
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
T
h
is
tr
an
s
f
o
r
m
atio
n
o
f
r
aw
im
ag
e
d
ata
in
to
a
s
tr
u
ct
u
r
ed
f
o
r
m
a
t
allo
wed
th
e
m
o
d
els
to
f
o
c
u
s
o
n
th
e
ess
en
tial
f
ea
tu
r
es
r
eq
u
i
r
ed
to
d
is
tin
g
u
i
s
h
b
etwe
en
h
ea
lth
y
an
d
d
is
ea
s
ed
leav
es.
B
y
em
p
h
asizin
g
t
h
ese
attr
ib
u
tes,
th
e
f
ea
tu
r
e
ex
tr
ac
tio
n
p
r
o
ce
s
s
en
h
an
ce
d
th
e
m
o
d
els'
ca
p
ac
ity
to
ac
cu
r
ately
class
if
y
d
is
ea
s
es,
s
ig
n
if
ican
tly
im
p
r
o
v
in
g
th
e
o
v
er
all
ef
f
ec
tiv
en
ess
o
f
th
e
class
if
icatio
n
p
r
o
c
ess
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
n
h
a
n
ci
n
g
p
l
a
n
t d
is
ea
s
e
d
etec
tio
n
u
s
in
g
ma
c
h
in
e
lea
r
n
in
g
a
p
p
r
o
a
ch
es fo
r
…
(
Ga
n
g
a
P
is
ka
)
2085
2
.
2
.
5
.
M
o
del
t
ra
ini
ng
Du
r
in
g
m
o
d
el
tr
ain
in
g
,
ea
ch
s
elec
ted
m
o
d
el
was
tr
ain
ed
o
n
th
e
d
ataset
o
v
er
3
0
ep
o
ch
s
,
allo
win
g
f
o
r
iter
ativ
e
lear
n
in
g
t
h
r
o
u
g
h
r
e
p
ea
ted
ex
p
o
s
u
r
e
to
th
e
tr
ai
n
in
g
d
ata.
T
h
e
m
o
d
els
m
ad
e
p
r
ed
ictio
n
s
a
n
d
co
n
tin
u
o
u
s
ly
ad
ju
s
ted
th
eir
p
ar
am
eter
s
b
ased
o
n
t
h
e
er
r
o
r
b
etwe
en
p
r
ed
icted
an
d
ac
tu
al
lab
els,
g
r
a
d
u
ally
im
p
r
o
v
in
g
p
atter
n
r
ec
o
g
n
itio
n
.
Key
p
er
f
o
r
m
a
n
ce
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
wer
e
m
o
n
ito
r
ed
th
r
o
u
g
h
o
u
t
th
e
tr
ain
in
g
p
r
o
ce
s
s
to
en
s
u
r
e
th
at
th
e
m
o
d
els
g
en
er
alize
d
well
a
n
d
av
o
id
e
d
o
v
e
r
f
itti
n
g
.
T
ec
h
n
i
q
u
es
s
u
ch
as
ea
r
ly
s
to
p
p
in
g
an
d
r
eg
u
lar
izatio
n
w
er
e
em
p
lo
y
e
d
wh
en
n
ec
ess
ar
y
to
f
u
r
th
e
r
o
p
tim
ize
m
o
d
el
p
er
f
o
r
m
an
ce
.
2
.
2
.
6
.
M
o
del
e
v
a
lua
t
io
n
M
o
d
e
l
e
v
a
l
u
at
i
o
n
wa
s
c
o
n
d
u
c
te
d
u
s
i
n
g
v
a
r
i
o
u
s
cl
a
s
s
i
f
i
c
a
ti
o
n
m
e
t
r
i
cs
t
o
c
o
m
p
a
r
e
t
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
f
i
v
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
o
d
e
ls
:
S
V
M
,
RF
,
l
o
g
i
s
t
i
c
r
e
g
r
es
s
i
o
n
,
X
G
B
o
o
s
t
,
a
n
d
C
N
N
.
T
h
e
m
o
d
e
l
s
w
e
r
e
e
v
a
l
u
a
t
ed
b
a
s
e
d
o
n
t
h
ei
r
a
c
c
u
r
ac
y
s
c
o
r
es
,
p
r
e
c
i
s
i
o
n
,
r
e
c
a
ll
,
a
n
d
F
1
-
s
c
o
r
e
f
o
r
e
a
c
h
cl
a
s
s
.
T
h
e
m
o
d
e
ls
w
er
e
r
a
n
k
e
d
b
a
s
e
d
o
n
t
h
e
i
r
a
c
c
u
r
a
c
y
s
c
o
r
es
,
w
it
h
C
N
N
a
c
h
i
e
v
i
n
g
t
h
e
h
i
g
h
es
t
a
c
cu
r
a
c
y
o
f
0
.
8
9
,
f
o
l
l
o
w
e
d
b
y
SV
M
(
0
.
7
0
)
,
X
GB
o
o
s
t
(
0
.
6
8
)
,
RF
(
0
.
6
4
)
,
a
n
d
l
o
g
i
s
ti
c
r
e
g
r
e
s
s
i
o
n
(
0
.
5
1
)
.
C
N
N
o
u
t
p
e
r
f
o
r
m
e
d
t
h
e
o
t
h
e
r
m
o
d
e
l
s
i
n
t
e
r
m
s
o
f
a
cc
u
r
a
c
y
,
p
r
e
c
i
s
i
o
n
,
r
e
c
al
l
,
a
n
d
F
1
-
s
c
o
r
e
f
o
r
e
a
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a
s
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n
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at
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ti
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f
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.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
co
m
p
ar
is
o
n
o
f
v
a
r
io
u
s
m
a
ch
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o
r
ith
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s
f
o
r
l
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f
d
is
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etec
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ea
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ig
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t
in
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ig
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ts
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to
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icac
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a
n
d
p
e
r
f
o
r
m
an
ce
ac
r
o
s
s
d
if
f
er
en
t
cr
o
p
ty
p
es.
T
ab
le
1
p
r
esen
ts
th
e
ac
cu
r
ac
y
ac
h
iev
ed
b
y
C
NN,
SVM,
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F,
X
G
B
o
o
s
t
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d
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g
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eg
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ess
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(
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R
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tin
g
leaf
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i
s
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s
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in
to
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ato
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s
o
y
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n
s
,
jav
a
p
lu
m
s
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ay
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d
m
an
g
o
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h
e
C
NN
m
o
d
el
e
x
h
ib
ited
th
e
h
ig
h
est
a
cc
u
r
ac
y
ac
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o
s
s
m
o
s
t
cr
o
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ty
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with
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o
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le
p
er
f
o
r
m
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ce
s
o
f
9
0
% f
o
r
to
m
ato
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7
8
% f
o
r
s
o
y
b
ea
n
s
,
a
n
d
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% f
o
r
jav
a
p
lu
m
s
.
T
ab
le
1
.
C
o
m
p
a
r
is
o
n
o
f
m
o
d
el
a
cc
u
r
ac
y
(
in
%)
M
o
d
e
l
To
ma
t
o
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o
y
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e
a
n
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a
p
l
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m
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y
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eaf
M
a
n
g
o
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N
N
90
78
88
79
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V
M
77
70
63
45
39
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68
70
63
45
37
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78
71
68
47
40
LR
66
52
51
35
25
T
h
e
SVM
m
o
d
el
also
d
em
o
n
s
tr
ated
co
m
p
etitiv
e
ac
c
u
r
ac
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,
p
ar
ticu
lar
ly
in
s
o
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b
ea
n
d
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s
e
d
etec
tio
n
,
ac
h
iev
in
g
7
0
%.
T
h
e
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an
d
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m
o
d
els
s
h
o
wed
m
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er
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te
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els,
wh
ile
t
h
e
L
R
m
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el
lag
g
e
d
b
eh
in
d
,
with
th
e
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west
ac
c
u
r
ac
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o
f
2
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%
f
o
r
m
an
g
o
es.
T
h
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f
in
d
in
g
s
illu
s
tr
ate
th
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ab
ilit
y
o
f
m
o
d
er
n
m
ac
h
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e
lear
n
i
n
g
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o
r
ith
m
s
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p
ar
ticu
lar
ly
C
NN
an
d
SVM,
to
r
eliab
ly
d
etec
t
leaf
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is
ea
s
es
in
v
ar
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s
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r
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ltu
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al
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s
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n
ad
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itio
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to
th
e
ac
cu
r
ac
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m
etr
ics
p
r
esen
ted
in
T
ab
le
1
,
Fig
u
r
e
3
illu
s
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ates
th
e
p
er
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o
r
m
an
ce
m
etr
ics
o
f
th
e
m
ac
h
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m
o
d
els
f
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p
la
n
t
d
is
ea
s
e
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etec
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n
.
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g
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r
o
v
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v
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u
al
r
ep
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er
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er
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els'
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f
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in
class
if
y
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g
d
if
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er
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t
d
is
ea
s
e
ty
p
es.
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h
e
c
o
m
b
in
atio
n
o
f
th
ese
q
u
an
titativ
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d
q
u
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s
m
en
ts
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er
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m
p
r
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en
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iv
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iew
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ch
m
o
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el's
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tr
en
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s
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k
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p
h
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m
p
o
r
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ap
p
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m
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em
en
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u
r
e
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s
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ates
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ar
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eter
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alu
ate
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el
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er
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o
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a
n
ce
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h
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
p
r
o
p
o
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ed
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tio
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m
ea
s
u
r
ed
th
r
o
u
g
h
f
o
u
r
m
etr
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r
ac
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r
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r
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m
atr
ix
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a
b
le
2
s
h
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ws
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e
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o
n
f
u
s
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m
a
tr
ix
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ased
o
n
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ich
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er
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a
lu
atio
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r
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eter
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ar
e
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lcu
lated
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alcu
lated
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itiv
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alse p
o
s
itiv
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f
alse n
eg
ativ
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an
d
tr
u
e
p
o
s
itiv
es.
T
h
e
ac
cu
r
ac
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d
eter
m
in
ed
as
(
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.
=
+
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(
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s
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ilar
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av
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m
p
u
ter
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ich
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e
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ess
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=
+
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ec
all
ca
n
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e
ex
p
r
ess
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as
(
3
)
.
=
+
(
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)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Fig
u
r
e
3
.
Per
f
o
r
m
an
c
e
m
etr
ics o
f
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els f
o
r
p
lan
t
d
is
ea
s
e
d
etec
tio
n
T
ab
le
2
.
C
o
n
f
u
s
io
n
m
atr
ix
A
c
t
u
a
l
p
o
si
t
i
v
e
A
c
t
u
a
l
n
e
g
a
t
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v
e
To
t
a
l
P
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e
d
i
c
t
e
d
p
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si
t
i
v
e
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P
F
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d
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c
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a
t
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v
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N
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N
F
N
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N
To
t
a
l
T
P
+F
N
F
P
+T
N
4.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
ex
p
l
o
r
es
th
e
u
s
e
o
f
m
ac
h
in
e
lear
n
i
n
g
tec
h
n
iq
u
es
lik
e
SVM,
C
NN,
RF
,
lo
g
is
tic
r
eg
r
ess
io
n
,
an
d
XGBo
o
s
t
to
class
if
y
p
lan
t
leaf
d
is
ea
s
es.
T
h
e
m
eth
o
d
u
s
es
a
cu
r
ated
d
atas
et
ca
lled
C
r
o
p
L
ea
f
I
n
s
ig
h
ts
,
wh
ich
in
clu
d
es
p
h
o
to
s
o
f
v
ar
io
u
s
p
lan
t
leav
es.
T
h
e
s
y
s
tem
'
s
ac
cu
r
ac
y
is
ex
p
ec
ted
to
in
cr
ea
s
e
o
v
er
tim
e
as
f
ea
tu
r
e
ex
t
r
ac
tio
n
a
p
p
r
o
ac
h
es
an
d
class
if
icatio
n
alg
o
r
ith
m
s
ar
e
u
s
ed
.
T
h
e
s
u
cc
ess
f
u
l
ad
o
p
tio
n
o
f
th
is
m
eth
o
d
h
as
p
o
s
itiv
e
im
p
licati
o
n
s
f
o
r
a
g
r
icu
ltu
r
al
o
p
er
atio
n
s
,
as
it
allo
ws
f
ar
m
er
s
to
m
ak
e
in
f
o
r
m
e
d
d
ec
is
io
n
s
ab
o
u
t
d
is
ea
s
e
m
an
ag
em
en
t
m
ea
s
u
r
es,
s
u
ch
as
f
u
n
g
icid
e
u
s
e.
T
h
is
p
r
o
ac
tiv
e
s
tr
ateg
y
p
r
o
te
cts
cr
o
p
h
ea
lth
an
d
in
cr
ea
s
es
ag
r
icu
ltu
r
al
s
u
s
tain
ab
ilit
y
.
T
h
e
r
esear
ch
er
s
an
tic
ip
ate
f
u
r
th
e
r
s
tu
d
y
to
im
p
r
o
v
e
an
d
o
p
tim
ize
th
e
s
y
s
tem
,
p
r
o
v
id
in
g
f
ar
m
er
s
with
r
eliab
le
d
is
ea
s
e
co
n
tr
o
l
to
o
ls
an
d
p
r
ac
tical
in
s
ig
h
ts
to
i
m
p
r
o
v
e
cr
o
p
o
u
t
p
u
t
an
d
co
n
tr
ib
u
te
to
g
lo
b
al
f
o
o
d
s
ec
u
r
ity
in
itiativ
es.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
au
th
o
r
s
th
an
k
o
u
r
co
lle
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u
es
f
r
o
m
Sh
ah
a
n
d
An
c
h
o
r
Ku
tch
h
i
E
n
g
in
ee
r
in
g
C
o
l
leg
e
wh
o
p
r
o
v
id
e
d
in
s
ig
h
t
an
d
ass
is
ta
n
ce
th
at
co
n
s
p
icu
o
u
s
ly
ass
is
ted
th
e
r
esear
ch
ca
r
r
ied
b
y
u
s
.
W
ith
o
u
t
th
eir
p
er
s
is
ten
t su
p
p
o
r
t,
co
n
d
u
ctin
g
r
esear
ch
wo
u
ld
n
o
t h
a
v
e
b
ee
n
p
o
s
s
ib
le.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
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(
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2087
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F
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R
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NC
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[
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