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d
ata
an
d
to
m
a
k
e
ju
d
g
m
en
ts
o
r
p
r
ed
ictio
n
s
o
n
th
eir
o
wn
,
with
o
u
t
h
u
m
a
n
in
ter
v
e
n
tio
n
o
r
ex
p
licit
p
r
o
g
r
am
m
i
n
g
.
Dee
p
lear
n
in
g
is
a
s
u
b
f
ield
o
f
m
ac
h
in
e
lear
n
in
g
th
at
ex
ce
ls
at
co
m
p
lex
task
s
lik
e
im
ag
e
i
d
en
tific
atio
n
[
5
]
.
I
t
u
s
es m
u
lti
-
lay
er
ed
n
eu
r
al
n
et
wo
r
k
s
to
ex
tr
ac
t in
s
ig
h
ts
f
r
o
m
b
ig
d
atasets
.
As
s
u
b
f
ield
s
o
f
ar
tific
ial
in
tellig
en
ce
,
m
ac
h
in
e
lear
n
in
g
,
an
d
d
ee
p
lear
n
in
g
h
av
e
f
ac
ilit
ated
th
e
cr
ea
tio
n
o
f
au
to
m
ated
s
y
s
tem
s
an
d
p
r
ed
ictiv
e
m
o
d
els,
wh
ich
h
av
e
h
ad
a
p
r
o
f
o
u
n
d
im
p
ac
t
o
n
a
n
u
m
b
er
o
f
in
d
u
s
tr
ies.
T
h
ese
tech
n
o
lo
g
i
es
g
r
ea
tly
im
p
r
o
v
e
th
e
ab
ilit
y
to
d
etec
t
an
d
d
iag
n
o
s
e
p
lan
t
d
is
ea
s
es
q
u
ick
ly
in
ag
r
icu
ltu
r
e.
T
h
is
is
ess
en
tia
l
f
o
r
d
ev
elo
p
in
g
an
d
im
p
lem
en
tin
g
m
a
n
ag
em
en
t
s
t
r
ateg
ies
th
at
ar
e
b
o
th
ef
f
ec
tiv
e
an
d
im
p
lem
en
ted
o
n
tim
e.
T
h
is
p
ap
er
ass
ess
e
s
th
e
s
tate
o
f
p
la
n
t
d
is
ea
s
e
d
et
ec
tio
n
u
s
in
g
d
ee
p
lear
n
in
g
a
n
d
m
ac
h
in
e
lear
n
in
g
,
co
m
p
a
r
in
g
an
d
co
n
tr
asti
n
g
d
if
f
er
en
t stra
teg
ies an
d
th
eir
r
e
lativ
e
ef
f
icac
y
[
6
]
.
Utilizin
g
th
ese
tech
n
o
lo
g
ies,
a
u
to
m
ated
m
eth
o
d
s
f
o
r
d
iag
n
o
s
in
g
p
la
n
t
d
is
ea
s
es
ca
n
b
e
cr
ea
ted
in
t
h
e
ag
r
icu
ltu
r
al
s
ec
to
r
.
T
o
h
elp
wi
th
tim
ely
in
ter
v
en
tio
n
an
d
m
in
im
ize
lo
s
s
es,
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
i
n
g
m
o
d
els
ca
n
an
aly
ze
p
r
o
p
er
tie
s
d
er
iv
ed
f
r
o
m
im
a
g
es
o
r
s
en
s
o
r
d
ata
to
ac
c
u
r
ately
d
iag
n
o
s
e
an
d
ca
teg
o
r
iz
e
v
ar
io
u
s
p
lan
t
d
is
ea
s
es.
T
h
e
p
u
r
p
o
s
e
o
f
th
is
r
esear
ch
is
t
o
s
u
r
v
ey
th
e
ex
is
tin
g
liter
atu
r
e
o
n
p
lan
t
d
is
ea
s
e
d
etec
tio
n
u
s
in
g
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
i
n
g
alg
o
r
ith
m
s
,
f
o
cu
s
in
g
o
n
im
p
o
r
tan
t
ap
p
r
o
ac
h
es,
th
eir
u
s
es,
an
d
r
elativ
e
ef
f
icac
y
[
7
]
,
[
8
]
.
Fo
llo
win
g
th
is
,
we
will
ex
am
in
e
r
ec
en
t
d
ev
elo
p
m
en
ts
in
ar
tific
ial
in
tellig
en
ce
an
d
d
ee
p
lear
n
i
n
g
m
eth
o
d
o
lo
g
ies,
in
clu
d
in
g
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs
)
,
an
d
d
iv
e
in
to
s
ev
er
al
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
,
with
an
e
m
p
h
asis
o
n
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
.
T
h
e
r
elativ
e
m
er
its
o
f
v
a
r
io
u
s
ap
p
r
o
ac
h
es
will
b
e
laid
b
ar
e
b
y
an
e
x
am
in
atio
n
o
f
th
em
.
T
h
e
p
ap
er
will
wr
ap
u
p
b
y
d
is
cu
s
s
in
g
f
u
t
u
r
e
p
r
o
s
p
ec
ts
an
d
h
o
w
th
ese
tech
n
o
lo
g
ies co
u
ld
b
e
in
teg
r
ated
in
t
o
p
r
ac
tical
f
ar
m
i
n
g
p
r
ac
tices [
9
]
,
[
1
0
]
.
2.
P
L
ANT D
I
S
E
AS
E
D
E
T
E
C
T
I
O
N
US
I
NG
M
ACH
I
N
E
L
E
ARN
I
NG
T
E
CH
NIQU
E
S
B
y
s
i
m
p
l
i
f
y
i
n
g
t
h
e
p
r
o
c
e
s
s
o
f
a
n
a
l
y
z
i
n
g
l
a
r
g
e
d
at
a
s
e
t
s
i
n
s
e
a
r
c
h
o
f
p
a
t
t
e
r
n
s
i
n
d
i
c
a
ti
v
e
o
f
d
i
f
f
e
r
e
n
t
p
l
a
n
t
h
e
a
l
t
h
s
i
t
u
at
i
o
n
s
,
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
h
a
s
s
u
b
s
ta
n
t
i
a
ll
y
e
n
h
a
n
c
ed
t
h
e
i
d
e
n
ti
f
i
c
at
i
o
n
o
f
p
l
a
n
t
d
is
e
as
e
s
.
I
n
o
r
d
e
r
t
o
f
o
r
e
c
a
s
t
t
h
e
s
p
r
e
a
d
o
f
d
i
s
e
as
es
a
n
d
c
a
t
e
g
o
r
i
z
e
t
h
e
m
a
c
c
o
r
d
i
n
g
t
o
f
e
a
t
u
r
e
s
r
e
t
r
i
e
v
e
d
f
r
o
m
p
i
c
t
u
r
e
s
,
s
e
n
s
o
r
d
a
t
a
,
o
r
o
t
h
e
r
s
o
u
r
c
e
s
,
t
h
e
s
e
m
e
t
h
o
d
s
u
s
e
a
l
g
o
r
i
t
h
m
s
t
h
a
t
h
a
v
e
b
e
e
n
t
r
a
i
n
e
d
o
n
a
n
n
o
t
a
t
e
d
d
a
t
a
.
T
h
i
s
s
e
ct
i
o
n
o
f
f
e
r
s
a
s
y
n
o
p
s
i
s
o
f
t
h
e
m
ai
n
m
a
c
h
in
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
u
s
e
d
f
o
r
p
l
a
n
t
d
i
s
e
as
e
d
i
a
g
n
o
s
is
,
t
o
g
e
t
h
e
r
w
i
t
h
a
n
e
x
a
m
i
n
a
t
i
o
n
o
f
t
h
e
i
r
r
es
p
e
c
ti
v
e
a
p
p
l
i
c
at
i
o
n
s
[
1
1
]
,
[
1
2
]
.
A
g
r
ic
u
l
t
u
r
a
l
s
o
i
l
f
e
r
t
il
i
t
y
w
as
e
x
a
m
i
n
e
d
u
s
i
n
g
m
ac
h
i
n
e
l
e
a
r
n
i
n
g
p
r
o
t
o
c
o
l
s
.
R
e
s
e
a
r
c
h
o
n
a
g
r
i
c
u
l
t
u
r
e
h
as
al
w
a
y
s
b
e
e
n
t
h
e
f
o
c
u
s
.
B
as
e
d
o
n
a
n
u
m
b
e
r
o
f
l
i
m
it
a
t
i
o
n
s
,
t
h
i
s
m
e
t
h
o
d
o
f
s
o
i
l
d
a
t
a
a
n
a
l
y
s
i
s
s
e
e
k
s
t
o
s
y
s
t
e
m
a
ti
c
a
ll
y
c
l
as
s
i
f
y
a
n
d
i
m
p
r
o
v
e
e
a
c
h
r
e
p
r
e
s
e
n
t
a
t
i
o
n
.
T
h
e
s
t
u
d
y
o
f
a
g
r
i
c
u
l
t
u
r
e
h
a
s
b
e
e
n
g
r
e
a
t
l
y
e
n
h
a
n
c
e
d
b
y
i
n
n
o
v
a
t
i
o
n
s
s
u
c
h
a
s
d
a
t
a
m
i
n
i
n
g
a
n
d
r
o
b
o
t
i
c
s
.
D
a
ta
m
i
n
i
n
g
h
a
s
m
a
n
y
p
o
t
e
n
t
i
a
l
u
s
e
s
,
a
n
d
t
h
e
r
e
i
s
c
o
m
m
e
r
c
i
a
l
s
o
f
t
w
a
r
e
a
v
ai
l
a
b
le
t
o
ad
d
r
e
s
s
s
p
e
c
i
f
i
c
n
ee
d
s
i
n
d
i
f
f
e
r
en
t
f
i
e
l
d
s
.
N
e
v
e
r
t
h
e
l
es
s
,
as
s
e
e
n
i
n
F
i
g
u
r
e
1
,
a
g
r
i
c
u
l
t
u
r
a
l
s
o
il
a
n
d
p
l
a
n
t
h
e
a
l
t
h
d
a
t
a
b
a
s
e
s
r
e
p
r
es
e
n
t
a
n
e
x
p
a
n
d
i
n
g
a
r
e
a
o
f
s
t
u
d
y
.
Fi
g
u
r
e
1
(
a
)
s
h
o
w
s
d
is
e
as
e
d
l
e
a
f
s
a
m
p
l
es
w
it
h
v
i
s
i
b
l
e
i
n
f
e
c
t
i
o
n
s
y
m
p
t
o
m
s
s
u
c
h
as
s
p
o
ts
a
n
d
d
i
s
c
o
l
o
r
a
ti
o
n
,
w
h
i
l
e
F
i
g
u
r
e
1
(
b
)
i
l
l
u
s
t
r
a
t
es
h
e
a
l
t
h
y
le
a
v
e
s
w
i
t
h
u
n
i
f
o
r
m
t
e
x
t
u
r
e
a
n
d
c
o
l
o
r
.
T
o
g
e
t
h
e
r
,
t
h
e
y
f
o
r
m
a
b
a
l
a
n
c
e
d
d
a
t
as
e
t
u
s
e
f
u
l
f
o
r
d
i
s
ti
n
g
u
i
s
h
i
n
g
b
e
t
w
ee
n
n
o
r
m
a
l
a
n
d
d
i
s
e
as
e
d
c
o
n
d
i
t
i
o
n
s
i
n
a
g
r
i
c
u
l
t
u
r
a
l
r
es
e
a
r
c
h
.
(
a)
(
b
)
Fig
u
r
e
1
.
Sam
p
le
im
a
g
es o
f
le
av
es: (
a)
d
is
ea
s
ed
an
d
(
b
)
h
ea
lth
y
T
h
e
m
eth
o
d
o
f
f
er
e
d
a
co
m
p
r
eh
en
s
iv
e
b
r
ea
k
d
o
wn
o
f
th
e
m
ac
h
in
e
lear
n
in
g
s
tr
ateg
y
e
m
p
lo
y
ed
to
class
if
y
p
lan
t
d
is
ea
s
e
s
y
m
p
to
m
s
.
Ho
wev
er
,
t
h
e
elem
en
ts
i
m
p
ac
tin
g
d
is
ea
s
e
d
etec
tio
n
w
er
e
n
o
t
ad
eq
u
ately
ca
p
tu
r
ed
b
y
th
e
tech
n
iq
u
e
.
T
h
er
e
n
ee
d
s
to
b
e
th
o
r
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u
g
h
ev
al
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atio
n
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d
ap
p
licatio
n
o
f
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e
m
ass
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e
v
o
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m
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o
f
d
ata
co
llected
in
r
elatio
n
to
cr
o
p
s
.
T
h
e
ap
p
r
o
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h
u
s
ed
to
p
r
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-
p
r
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ce
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s
th
e
in
p
u
t im
ag
e
is
s
h
o
wn
in
Fig
u
r
e
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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Fig
u
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2
.
T
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p
ical
p
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r
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o
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id
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tif
y
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cr
o
p
s
B
ec
au
s
e
o
f
th
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f
ec
tiv
e
n
ess
in
class
if
icatio
n
task
s
,
s
u
p
p
o
r
t
v
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to
r
m
ac
h
in
es
(
SVM)
ar
e
f
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e
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tly
u
s
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to
i
d
en
tify
p
lan
t
d
is
ea
s
es.
T
o
d
i
f
f
er
en
tiate
b
etwe
en
d
if
f
er
en
t
class
es
in
th
e
f
ea
tu
r
e
s
p
ac
e,
SVM
ch
o
o
s
e
th
e
b
est
h
y
p
er
p
la
n
e.
Alth
o
u
g
h
th
ese
ap
p
r
o
ac
h
es
ar
e
b
est
u
s
e
d
f
o
r
b
i
n
ar
y
class
if
icatio
n
,
th
e
y
ca
n
b
e
m
o
d
if
ied
to
h
an
d
le
s
itu
atio
n
s
with
m
o
r
e
th
an
o
n
e
class
u
tili
z
in
g
s
tr
at
eg
ies
lik
e
o
n
e
-
v
er
s
u
s
-
o
n
e
o
r
o
n
e
-
v
e
r
s
u
s
-
all
[
1
3
]
.
I
n
th
e
f
ield
o
f
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
,
SVM
ar
e
an
ef
f
e
ctiv
e
to
o
l
f
o
r
d
is
tin
g
u
is
h
in
g
b
etwe
en
h
ea
lth
y
an
d
s
ick
p
lan
ts
.
T
h
e
y
l
o
o
k
at
t
h
e
s
h
ap
e,
tex
tu
r
e,
an
d
c
o
lo
r
o
f
th
e
lea
v
es
in
th
e
p
h
o
t
o
s
.
C
r
o
p
s
lik
e
t
o
m
ato
es,
cu
cu
m
b
er
s
,
an
d
g
r
ap
es
h
a
v
e
b
ee
n
s
u
cc
ess
f
u
lly
d
is
ea
s
ed
u
s
in
g
SVM,
wh
ich
h
a
v
e
ac
h
iev
ed
h
i
g
h
r
ates
o
f
ac
cu
r
ac
y
.
SVM
s
ef
f
icac
y
is
h
i
g
h
ly
d
ep
en
d
en
t
o
n
t
h
e
k
e
r
n
el
f
u
n
ctio
n
o
p
tim
izatio
n
an
d
p
a
r
am
eter
ch
o
ice
[
1
4
]
,
[
1
5
]
.
An
a
d
v
an
ce
d
m
eth
o
d
i
n
en
s
em
b
le
lear
n
in
g
,
r
an
d
o
m
f
o
r
est
(
R
F)
co
m
b
in
e
m
an
y
d
ec
is
io
n
tr
ee
s
to
im
p
r
o
v
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
T
h
e
f
in
al
f
o
r
ec
ast
is
o
b
tain
ed
b
y
ad
d
i
n
g
to
g
eth
e
r
th
e
r
esu
lts
f
r
o
m
all
th
e
tr
ee
s
in
th
e
f
o
r
est,
ty
p
ically
b
y
m
ajo
r
ity
v
o
tin
g
,
a
n
d
ea
c
h
tr
ee
in
th
e
f
o
r
est
is
b
u
ilt
u
s
in
g
a
r
an
d
o
m
ly
s
elec
ted
p
o
r
tio
n
o
f
th
e
d
ata.
Du
e
to
it
s
r
o
b
u
s
tn
ess
an
d
ab
ilit
y
to
ef
f
icien
tly
h
an
d
le
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata,
RF
f
in
d
s
ex
ten
s
iv
e
u
s
ag
e
in
th
e
d
etec
tio
n
o
f
p
lan
t
d
is
ea
s
es.
T
h
ey
ca
n
b
e
u
s
ef
u
l
f
o
r
id
e
n
tify
in
g
s
u
b
tle
s
ig
n
s
o
f
illn
ess
s
in
ce
th
ey
m
ay
in
clu
d
e
c
o
m
p
l
ex
in
ter
ac
tio
n
s
b
etwe
en
attr
ib
u
tes.
Dis
ea
s
e
d
iag
n
o
s
is
in
wh
ea
t,
m
aize
,
an
d
r
ice
cr
o
p
s
is
a
c
o
m
m
o
n
ar
ea
o
f
r
e
s
ea
r
ch
wh
er
e
R
Fs
ex
ce
l,
o
f
te
n
o
u
t
p
er
f
o
r
m
in
g
in
d
i
v
id
u
al
d
ec
is
io
n
tr
ee
m
o
d
els
[
1
6
]
.
On
e
ef
f
ec
tiv
e
a
n
d
f
u
n
d
a
m
en
tal
tech
n
iq
u
e
is
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
,
wh
ich
s
o
r
ts
d
ata
p
o
in
ts
in
th
e
f
ea
tu
r
e
s
p
ac
e
b
y
t
h
e
m
o
s
t
co
m
m
o
n
class
o
f
th
eir
KNN
.
T
h
e
in
s
tan
ce
-
b
ased
an
d
n
o
n
-
p
a
r
am
etr
ic
n
atu
r
e
o
f
KNN
m
ak
es
it
ea
s
y
to
im
p
lem
en
t
an
d
u
n
d
er
s
tan
d
.
I
n
o
r
d
er
to
d
etec
t
p
lan
t
d
is
ea
s
es,
KNN
co
m
p
ar
es
n
ew
s
am
p
les
to
a
d
atab
ase
o
f
lab
e
lled
o
cc
u
r
r
e
n
ce
s
an
d
u
s
es
th
at
in
f
o
r
m
atio
n
to
class
if
y
ailm
en
ts
.
B
y
an
aly
zin
g
f
ea
tu
r
es
o
b
tain
ed
f
r
o
m
p
ictu
r
e
s
,
KNN
h
as
b
ee
n
u
s
ed
to
d
ete
ct
illn
ess
es
in
citr
u
s
f
r
u
its
an
d
ap
p
les.
Alth
o
u
g
h
KNN
is
ca
p
ab
le
o
f
ac
h
ie
v
in
g
h
ig
h
le
v
els
o
f
ac
cu
r
ac
y
,
its
ef
f
icien
cy
is
af
f
ec
ted
b
y
th
e
q
u
ality
o
f
th
e
f
ea
tu
r
e
s
p
ac
e
an
d
th
e
c
h
o
ice
o
f
k
[
1
7
]
.
On
e
c
o
m
m
o
n
s
tatis
tical
m
eth
o
d
f
o
r
b
in
ar
y
class
if
icati
o
n
task
s
is
L
R
.
B
y
an
aly
zin
g
in
p
u
t
d
ata
an
d
class
if
y
in
g
d
is
ea
s
e
p
r
esen
ce
o
r
a
b
s
en
ce
,
L
R
is
a
u
s
ef
u
l
m
eth
o
d
f
o
r
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
.
Alth
o
u
g
h
L
R
is
s
im
p
le,
it
s
h
o
ws
g
r
ea
t
s
u
cc
es
s
in
s
o
m
e
s
itu
atio
n
s
s
in
ce
it
i
s
ea
s
y
to
u
n
d
er
s
tan
d
an
d
u
s
e.
Data
s
ets
co
v
er
in
g
s
e
v
er
al
p
lan
t
h
ea
lth
m
ea
s
u
r
es,
s
u
ch
as
leaf
s
h
ap
e,
te
x
tu
r
e,
a
n
d
co
lo
r
,
h
av
e
b
ee
n
s
u
b
jecte
d
to
lin
ea
r
r
e
g
r
ess
io
n
m
o
d
els.
B
y
co
r
r
elatin
g
th
ese
m
ar
k
er
s
with
th
e
p
r
o
b
a
b
ilit
y
o
f
illn
ess
o
cc
u
r
r
en
ce
,
L
R
p
r
o
v
id
es
a
s
im
p
le
an
d
ef
f
ec
tiv
e
to
o
l
f
o
r
p
r
elim
i
n
ar
y
d
is
ea
s
e
s
cr
ee
n
in
g
.
B
u
t
th
e
lin
ea
r
d
ec
is
io
n
b
o
u
n
d
ar
y
m
ay
lim
it its
ef
f
icac
y
,
m
ak
i
n
g
it u
n
f
it f
o
r
co
m
p
le
x
n
o
n
lin
ea
r
p
r
o
b
lem
s
[
1
8
]
,
[
1
9
]
.
2
.
1
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
L
R
s
er
v
es
as
a
f
o
u
n
d
atio
n
a
l
s
tati
s
tical
tech
n
iq
u
e
co
m
m
o
n
ly
ap
p
lied
i
n
b
in
ar
y
class
if
icatio
n
ch
allen
g
es,
s
u
ch
as th
e
id
en
tifi
ca
tio
n
o
f
p
lan
t d
is
ea
s
es.
E
v
en
th
o
u
g
h
it is
less
co
m
p
lex
th
an
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
i
n
g
m
eth
o
d
s
,
L
R
co
n
tin
u
es
to
b
e
an
im
p
o
r
tan
t
to
o
l
b
ec
a
u
s
e
o
f
its
clar
ity
,
ef
f
icien
cy
,
an
d
ef
f
ec
tiv
en
ess
in
s
p
ec
if
ic
ap
p
licatio
n
s
.
L
R
esti
m
ate
s
th
e
lik
elih
o
o
d
th
at
a
s
p
ec
if
ic
in
p
u
t
is
a
s
s
o
ciate
d
wi
th
a
ce
r
tain
class
.
I
n
co
n
tr
ast
to
lin
ea
r
r
eg
r
ess
io
n
,
wh
ich
f
o
r
ec
asts
co
n
tin
u
o
u
s
v
alu
es,
L
R
est
im
ates
th
e
lik
elih
o
o
d
o
f
a
b
in
ar
y
o
u
tco
m
e.
T
h
e
m
o
d
el
em
p
lo
y
s
th
e
lo
g
is
tic
f
u
n
ctio
n
,
co
m
m
o
n
l
y
r
ef
er
r
ed
to
as
th
e
s
ig
m
o
id
f
u
n
ctio
n
,
to
co
n
v
er
t
p
r
ed
icted
v
alu
es
in
to
p
r
o
b
a
b
ilit
ies
r
an
g
in
g
f
r
o
m
0
to
1
.
T
h
e
lo
g
is
tic
f
u
n
ctio
n
is
ch
ar
ac
ter
ized
as
(
1
)
an
d
(
2
)
.
(
)
=
1
+
−
1
(
1
)
w
h
er
e
z
is
a
lin
ea
r
co
m
b
in
atio
n
o
f
th
e
in
p
u
t
f
ea
tu
r
es:
=
0
+
1
×
1
+
2
×
2
+
⋯
+
×
=
0
+
1
×
1
+
2
×
2
+
⋯
+
×
(
2
)
h
er
e
,
0
is
th
e
in
ter
ce
p
t,
ar
e
th
e
co
ef
f
icien
ts
f
o
r
t
h
e
in
p
u
t f
ea
tu
r
es
,
an
d
is
th
e
n
u
m
b
er
o
f
f
ea
tu
r
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
co
mp
r
eh
en
s
ive
imp
r
ess
io
n
o
n
id
en
tifyin
g
p
la
n
t d
is
ea
s
es u
s
in
g
ma
ch
in
e
lea
r
n
in
g
…
(
R
a
vi
ka
n
th
Mo
tu
p
a
lli
)
4697
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h
e
L
R
m
o
d
e
l
c
a
l
c
u
l
a
t
es
t
h
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co
e
f
f
i
c
i
e
n
ts
i
n
o
r
d
e
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t
o
o
p
t
i
m
i
ze
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h
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k
e
l
i
h
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o
d
o
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t
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s
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a
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i
s
a
p
p
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ic
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o
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p
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a
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t
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i
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ec
t
i
o
n
t
h
r
o
u
g
h
t
h
e
u
t
i
l
i
z
a
t
i
o
n
o
f
f
e
a
t
u
r
e
s
d
e
r
i
v
e
d
f
r
o
m
p
l
a
n
t
i
m
a
g
e
s
o
r
s
e
n
s
o
r
d
a
t
a
.
T
h
e
c
h
a
r
a
c
t
e
r
is
t
i
cs
m
a
y
e
n
c
o
m
p
a
s
s
co
l
o
r
,
t
e
x
t
u
r
e
,
s
h
a
p
e
,
a
n
d
s
p
e
c
tr
a
l
d
a
t
a
t
h
a
t
s
i
g
n
i
f
y
t
h
e
e
x
i
s
t
e
n
c
e
o
f
a
d
is
e
as
e
[
2
0
]
,
[
2
1
]
.
T
h
e
p
r
o
c
e
d
u
r
e
t
y
p
i
c
a
l
l
y
e
n
c
o
m
p
a
s
s
es
t
h
e
s
e
s
t
a
g
es
:
i
)
g
at
h
e
r
i
n
g
a
n
d
p
r
e
p
a
r
i
n
g
d
a
t
a
:
g
at
h
e
r
a
c
o
l
l
ec
t
i
o
n
o
f
p
l
a
n
t
i
m
a
g
es
o
r
s
e
n
s
o
r
d
a
t
a
,
a
n
d
c
a
r
r
y
o
u
t
p
r
e
p
r
o
c
ess
i
n
g
t
o
i
d
e
n
t
i
f
y
a
n
d
e
x
t
r
a
c
t
p
e
r
ti
n
e
n
t
f
e
a
t
u
r
es
.
T
h
i
s
c
o
u
l
d
i
n
v
o
l
v
e
m
et
h
o
d
s
lik
e
i
m
a
g
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s
e
g
m
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n
t
a
t
i
o
n
,
f
e
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x
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r
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c
t
i
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n
,
a
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d
n
o
r
m
a
l
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a
t
i
o
n
;
ii
)
f
e
a
t
u
r
e
s
e
le
c
t
i
o
n
:
d
e
t
e
r
m
i
n
e
a
n
d
c
h
o
o
s
e
t
h
e
m
o
s
t
p
e
r
t
i
n
e
n
t
f
e
a
t
u
r
es
t
h
a
t
a
i
d
i
n
t
h
e
c
l
a
s
s
i
f
ic
a
t
i
o
n
o
f
d
is
e
as
e
s
.
F
e
atu
r
e
s
e
l
e
ct
i
o
n
m
a
y
r
e
l
y
o
n
e
x
p
e
r
t
i
s
e
i
n
t
h
e
f
ie
l
d
o
r
u
t
i
li
z
e
a
u
t
o
m
a
t
e
d
t
e
c
h
n
i
q
u
es
l
i
k
e
r
e
c
u
r
s
i
v
e
f
ea
t
u
r
e
el
i
m
i
n
ati
o
n
;
i
ii
)
t
r
ai
n
i
n
g
t
h
e
m
o
d
e
l
:
d
iv
i
d
e
t
h
e
d
at
a
s
et
i
n
t
o
t
r
ai
n
i
n
g
a
n
d
t
e
s
t
i
n
g
s
u
b
s
e
ts
.
U
t
i
li
z
e
t
h
e
t
r
a
i
n
i
n
g
s
et
t
o
d
e
v
el
o
p
t
h
e
L
R
m
o
d
e
l
,
i
n
c
o
r
p
o
r
a
t
in
g
t
h
e
e
x
t
r
a
c
t
e
d
f
e
a
t
u
r
es
as
i
n
p
u
t
v
a
r
i
a
b
l
es
a
n
d
t
h
e
d
i
s
e
as
e
l
a
b
el
s
as
t
h
e
ta
r
g
e
t
o
u
tp
u
t
s
;
a
n
d
i
v
)
m
o
d
e
l
e
v
a
l
u
a
ti
o
n
:
a
s
s
e
s
s
t
h
e
m
o
d
e
l'
s
p
e
r
f
o
r
m
a
n
c
e
u
s
i
n
g
t
h
e
t
es
ti
n
g
s
e
t
.
T
y
p
i
ca
l
e
v
a
l
u
at
i
o
n
m
e
t
r
ic
s
f
o
r
b
i
n
a
r
y
c
l
a
s
s
i
f
i
c
at
i
o
n
e
n
c
o
m
p
a
s
s
ac
c
u
r
a
c
y
,
p
r
e
c
is
i
o
n
,
r
e
ca
l
l
,
F
1
-
s
c
o
r
e
,
a
n
d
t
h
e
a
r
e
a
u
n
d
e
r
t
h
e
r
e
c
e
i
v
e
r
o
p
e
r
a
t
i
n
g
c
h
a
r
a
c
t
e
r
i
s
ti
c
c
u
r
v
e
(
A
UC
-
R
OC
)
.
L
R
c
o
n
ti
n
u
e
s
t
o
b
e
a
n
i
m
p
o
r
t
a
n
t
m
e
t
h
o
d
f
o
r
d
e
t
e
c
t
i
n
g
p
l
a
n
t
d
i
s
e
a
s
e
s
,
es
p
e
c
ia
l
l
y
w
h
e
n
f
a
ct
o
r
s
li
k
e
s
i
m
p
l
i
ci
ty
,
i
n
t
e
r
p
r
e
t
a
b
i
li
t
y
,
a
n
d
e
f
f
i
ci
e
n
c
y
a
r
e
c
r
u
c
i
a
l
.
T
h
e
u
s
e
o
f
t
h
i
s
a
p
p
r
o
a
c
h
,
i
n
c
o
n
j
u
n
c
t
i
o
n
w
i
t
h
m
o
r
e
s
o
p
h
i
s
t
i
c
at
e
d
t
e
c
h
n
i
q
u
e
s
,
c
a
n
g
r
e
a
tl
y
e
n
h
a
n
c
e
s
u
s
t
ai
n
a
b
le
a
g
r
i
c
u
l
t
u
r
e
t
h
r
o
u
g
h
t
h
e
f
a
c
i
li
t
a
ti
o
n
o
f
e
a
r
l
y
a
n
d
p
r
e
c
i
s
e
d
is
e
as
e
i
d
e
n
t
i
f
i
c
at
i
o
n
a
n
d
m
a
n
a
g
e
m
e
n
t
[
2
2
]
,
[
2
3
]
.
2
.
2
.
Art
if
ici
a
l
inte
llig
ence
a
nd
deep
lea
rning
-
ba
s
e
d pla
nt
dis
ea
s
e
det
ec
t
io
n
R
ec
en
t
d
ev
elo
p
m
en
ts
in
ar
tifi
cial
in
tellig
en
ce
an
d
d
ee
p
lear
n
in
g
h
av
e
g
r
ea
tly
im
p
r
o
v
ed
th
e
p
r
ec
is
io
n
an
d
ef
f
ec
tiv
e
n
ess
o
f
to
o
ls
u
s
e
d
to
d
etec
t
p
la
n
t
d
is
ea
s
es.
W
h
en
it
co
m
es
to
id
e
n
tify
in
g
a
n
d
class
if
y
in
g
p
lan
t
d
is
ea
s
es
f
r
o
m
p
h
o
to
s
an
d
o
th
er
d
ata
s
o
u
r
ce
s
,
th
ese
tech
n
o
lo
g
ies
esp
ec
ially
d
ee
p
lear
n
in
g
m
o
d
els
lik
e
C
NNs
ar
e
in
d
is
p
en
s
ab
le.
I
n
t
h
is
s
ec
tio
n
,
we
will
tak
e
a
lo
o
k
at
h
o
w
d
ee
p
lear
n
in
g
an
d
ar
tifi
cial
in
tellig
en
ce
ar
e
h
elp
in
g
with
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
.
W
e
will
h
i
g
h
lig
h
t
s
o
m
e
o
f
t
h
e
m
o
s
t
im
p
o
r
tan
t
m
eth
o
d
s
,
ap
p
licatio
n
s
,
an
d
r
ec
e
n
t
d
ev
e
lo
p
m
en
ts
in
th
is
f
ield
b
o
th
[
2
4
]
,
[
2
5
]
.
M
u
ltip
le
co
m
p
u
tati
o
n
al
ap
p
r
o
ac
h
es
to
p
atter
n
r
ec
o
g
n
itio
n
,
d
ec
is
io
n
-
m
ak
in
g
,
a
n
d
d
ata
-
d
r
iv
en
lea
r
n
in
g
m
ak
e
u
p
ar
tific
ial
in
tel
lig
en
ce
.
Ar
tific
ial
in
tellig
en
ce
'
s
d
ee
p
lear
n
in
g
s
u
b
f
ield
u
s
es
m
u
lti
-
lay
er
ed
n
eu
r
al
n
etwo
r
k
s
t
o
d
is
co
v
e
r
n
ew
f
ea
tu
r
es
an
d
lear
n
o
n
its
o
wn
f
r
o
m
r
aw
d
ata.
I
m
ag
e
id
en
tific
atio
n
an
d
class
if
icatio
n
ar
e
two
ex
am
p
les
o
f
d
if
f
icu
lt
jo
b
s
th
at
d
ee
p
lear
n
in
g
e
x
ce
ls
at.
Usi
n
g
C
NN
ar
ch
itectu
r
es,
d
ee
p
lear
n
in
g
m
o
d
els
h
a
v
e
s
h
o
wn
ex
c
ep
tio
n
al
ac
cu
r
ac
y
in
d
is
ea
s
e
id
en
tific
atio
n
in
p
lan
t
s
.
B
ec
au
s
e
o
f
its
ab
ilit
y
t
o
le
ar
n
s
p
atial
h
ier
a
r
ch
ies
o
f
in
f
o
r
m
atio
n
f
r
o
m
in
p
u
t
v
is
u
als
in
an
au
to
n
o
m
o
u
s
a
n
d
f
lex
ib
le
m
an
n
e
r
,
C
NN
s
ar
e
v
er
y
g
o
o
d
at
r
ec
o
g
n
izin
g
illn
ess
es
th
r
o
u
g
h
p
h
o
to
g
r
ap
h
s
.
W
h
en
it c
o
m
es t
o
d
ee
p
lear
n
i
n
g
m
o
d
els,
im
ag
e
p
r
o
ce
s
s
in
g
is
wh
er
e
th
e
C
NN
class
r
ea
lly
s
h
in
es
.
C
o
n
v
o
lu
tio
n
al,
p
o
o
lin
g
,
an
d
f
u
lly
co
n
n
ec
ted
lay
er
s
m
a
k
e
u
p
th
e
ar
ch
itectu
r
e
.
E
ac
h
s
u
cc
ess
iv
e
lay
er
r
ef
i
n
es
th
e
in
co
m
in
g
d
ata
b
y
h
ig
h
lig
h
tin
g
k
ey
ch
ar
ac
ter
is
tics
an
d
p
atter
n
s
.
T
h
e
in
p
u
t
im
ag
e'
s
ed
g
es,
tex
tu
r
es,
an
d
f
o
r
m
s
ar
e
d
etec
te
d
b
y
co
n
v
o
lu
tio
n
al
lay
er
s
with
th
e
u
s
e
o
f
f
i
lter
s
.
A
f
ea
tu
r
e
m
ap
is
cr
ea
ted
f
o
r
ea
c
h
f
ilter
th
at
h
elp
s
to
f
in
d
p
atter
n
s
in
th
e
im
ag
e.
Po
o
lin
g
lay
er
s
u
s
e
d
o
wn
s
am
p
lin
g
to
d
ec
r
ea
s
e
th
e
s
p
atial
d
im
en
s
io
n
s
o
f
f
ea
tu
r
e
m
a
p
s
,
k
ee
p
i
n
g
im
p
o
r
t
an
t
p
r
o
p
er
ties
wh
ile
r
ed
u
cin
g
co
m
p
u
tatio
n
al
co
m
p
lex
ity
.
T
h
e
last
p
r
ed
ictio
n
s
ar
e
m
ad
e
b
y
f
u
lly
c
o
n
n
e
cted
l
ay
er
s
u
s
in
g
th
e
d
ata
f
r
o
m
t
h
e
co
n
v
o
l
u
tio
n
al
an
d
p
o
o
lin
g
lay
er
s
.
Stan
d
ar
d
ly
u
s
ed
f
o
r
g
r
o
u
p
i
n
g
.
T
o
im
p
r
o
v
e
th
e
ir
p
er
f
o
r
m
an
ce
as
p
lan
t
d
is
ea
s
e
d
etec
to
r
s
,
C
NN
s
m
ay
tr
ain
o
n
th
eir
o
wn
a
n
d
ex
tr
ac
t r
elev
an
t
f
ea
tu
r
es f
r
o
m
p
h
o
to
s
,
d
o
in
g
awa
y
with
th
e
n
ec
ess
ity
f
o
r
h
u
m
a
n
f
ea
tu
r
e
en
g
in
ee
r
in
g
.
2
.
3
.
Co
nv
o
lutio
n
neura
l net
wo
rk
On
e
class
o
f
s
o
p
h
is
ticated
lea
r
n
in
g
m
o
d
els
d
ev
elo
p
ed
s
p
ec
i
f
ically
f
o
r
th
e
ex
am
in
atio
n
o
f
s
tr
u
ctu
r
ed
g
r
id
d
ata,
s
u
ch
as
p
ictu
r
es,
ar
e
C
NN
s
.
T
h
e
s
y
s
tem
'
s
s
ev
er
al
lay
er
s
ca
n
lear
n
to
r
ec
o
g
n
ize
h
ier
a
r
ch
ical
elem
en
ts
in
p
h
o
to
s
o
n
th
eir
o
wn
.
T
h
e
ab
ilit
y
o
f
C
NN
s
to
d
etec
t su
b
tle
ch
an
g
es in
d
is
ea
s
e
s
y
m
p
to
m
s
h
as led
to
th
eir
im
p
r
ess
iv
e
p
er
f
o
r
m
a
n
ce
in
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
.
An
ex
am
p
le
o
f
a
C
NN
d
esig
n
f
o
r
p
lan
t
d
is
ea
s
e
d
etec
tio
n
m
ig
h
t
h
av
e
c
o
n
v
o
lu
t
io
n
al
lay
er
s
f
o
r
f
ea
tu
r
e
ex
tr
ac
t
io
n
,
p
o
o
lin
g
la
y
er
s
f
o
r
d
o
wn
-
s
am
p
lin
g
,
a
n
d
f
u
lly
co
n
n
ec
ted
lay
er
s
f
o
r
class
if
icatio
n
.
Mo
d
els
f
o
r
p
la
n
t
d
is
ea
s
e
d
etec
tio
n
h
av
e
b
ee
n
im
m
en
s
el
y
en
h
an
ce
d
b
y
th
e
u
s
e
o
f
tr
an
s
f
er
lear
n
in
g
,
wh
ich
en
tails
r
ef
in
in
g
a
p
r
e
-
tr
ain
ed
C
NN
o
n
a
p
ar
ticu
lar
d
ataset.
I
m
p
r
ess
iv
e
ac
cu
r
ac
y
in
d
etec
tin
g
d
is
ea
s
es
s
u
ch
as
p
o
wd
er
y
m
ild
ew,
r
u
s
t,
an
d
b
a
cter
ial
b
lig
h
t
h
as
b
ee
n
ac
h
iev
ed
b
y
C
NN
s
,
wh
ich
h
av
e
s
h
o
wn
ef
f
icien
cy
ac
r
o
s
s
s
ev
er
al
p
lan
t
s
p
ec
ies.
Fo
r
t
h
e
p
u
r
p
o
s
e
o
f
d
ev
elo
p
i
n
g
ef
f
icien
t
s
y
s
tem
s
f
o
r
d
etec
tin
g
p
lan
t
d
is
ea
s
es,
C
NN
s
ar
e
th
e
id
ea
l c
h
o
ice
b
ec
au
s
e
to
th
eir
s
ca
lab
ilit
y
an
d
v
er
s
atility
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
t
is
clea
r
th
at
th
er
e
ar
e
ad
v
an
tag
es
an
d
d
is
ad
v
an
tag
es
to
b
o
th
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es
wh
en
it
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o
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n
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g
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t
d
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o
m
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s
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e.
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aits
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ates
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u
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.
Evaluation Warning : The document was created with Spire.PDF for Python.