I
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S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
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t
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I
J
-
AI
)
Vo
l.
15
,
No
.
1
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Feb
r
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2
0
2
6
,
p
p
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998
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1
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h
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//ij
a
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A review
of mo
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f
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pla
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disea
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a
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Dec
22
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P
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t
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a
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id
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n
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fica
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d
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rit
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p
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a
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d
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t
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n
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g
h
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ield
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a
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d
s
u
sta
in
a
b
le
fa
rm
in
g
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ra
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ti
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e
se
p
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ss
e
s
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v
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h
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ise
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se
s
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d
isti
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ish
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e
n
c
ro
p
s
a
n
d
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s
in
a
g
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field
s.
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it
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l
m
e
t
h
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s
fo
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m
a
n
a
g
in
g
th
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se
c
h
a
ll
e
n
g
e
s
a
re
o
ften
lab
o
r
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in
ten
siv
e
,
p
ro
n
e
to
e
rro
rs,
a
n
d
e
n
v
ir
o
n
m
e
n
tall
y
u
n
su
sta
in
a
b
le,
n
e
c
e
ss
i
tatin
g
th
e
d
e
v
e
lo
p
m
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n
t
o
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a
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t
o
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ted
,
a
c
c
u
ra
te,
a
n
d
sc
a
lab
le
s
o
lu
ti
o
n
s.
T
h
is
su
r
v
e
y
p
ro
v
id
e
s
a
c
o
m
p
re
h
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n
siv
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re
v
iew
o
f
th
e
sta
te
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of
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a
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p
p
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s
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l
-
b
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se
d
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io
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d
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n
d
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tral
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se
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m
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th
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s,
a
n
d
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lu
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tes
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ffe
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ss
in
v
a
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u
s
a
g
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ra
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c
o
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tex
ts.
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d
i
ti
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a
ll
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i
t
id
e
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ti
fies
si
g
n
i
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t
c
h
a
ll
e
n
g
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s
s
u
c
h
a
s
d
a
ta
sc
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rc
it
y
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m
o
d
e
l
g
e
n
e
ra
li
z
a
ti
o
n
,
a
n
d
c
o
m
p
u
tatio
n
a
l
c
o
n
stra
i
n
ts,
w
h
il
e
p
r
o
p
o
sin
g
p
o
ten
ti
a
l
re
se
a
rc
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d
irec
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s
to
a
d
d
re
ss
th
e
se
g
a
p
s.
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e
fi
n
d
i
n
g
s
a
im
t
o
g
u
i
d
e
fu
t
u
re
re
se
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h
in
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lo
p
in
g
m
o
re
r
o
b
u
st
a
n
d
i
n
te
rp
re
tab
le
m
o
d
e
ls
th
a
t
c
a
n
b
e
d
e
p
lo
y
e
d
i
n
re
a
l
-
wo
rld
a
g
ricu
l
tu
ra
l
e
n
v
iro
n
m
e
n
ts,
u
lt
ima
tely
c
o
n
tri
b
u
ti
n
g
to
m
o
re
e
fficie
n
t,
p
re
c
ise
,
a
n
d
su
sta
in
a
b
le
fa
rm
in
g
p
ra
c
ti
c
e
s.
K
ey
w
o
r
d
s
:
Dee
p
lear
n
in
g
Hig
h
cr
o
p
y
ield
s
Plan
t d
is
ea
s
e
id
en
tific
atio
n
Su
s
tain
ab
le
f
ar
m
in
g
p
r
ac
tices
W
ee
d
d
etec
tio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Mo
h
am
m
ad
Naseer
a
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
ee
r
in
g
,
Ma
lla
R
ed
d
y
E
n
g
in
ee
r
i
n
g
C
o
lleg
e
f
o
r
W
o
m
en
Hy
d
er
ab
ad
,
I
n
d
ia
E
m
ail: m
d
n
aseer
a@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Pre
cisi
o
n
f
ar
m
in
g
an
d
p
lan
t
p
h
en
o
t
y
p
in
g
d
ep
e
n
d
o
n
th
e
ac
cu
r
ate
d
iag
n
o
s
is
o
f
p
lan
t
d
is
ea
s
es.
A
s
ig
n
if
ican
t
q
u
an
tity
o
f
d
ata,
in
f
o
r
m
atio
n
,
an
d
tech
n
o
lo
g
y
ar
e
in
clu
d
ed
in
t
h
e
two
d
o
m
ain
s
.
Pre
cisi
o
n
ag
r
icu
ltu
r
e
is
n
o
t
a
g
o
o
d
f
it
f
o
r
tr
ad
itio
n
al
p
lan
t
d
is
ea
s
e
d
iag
n
o
s
is
an
d
m
o
n
ito
r
in
g
tech
n
iq
u
es
s
in
ce
th
ey
ar
e
co
s
tly
,
tim
e
-
co
n
s
u
m
in
g
,
an
d
d
ep
en
d
e
n
t
o
n
h
u
m
an
v
is
u
al
ex
am
in
atio
n
.
Fu
r
t
h
er
m
o
r
e,
it
is
an
ticip
ated
th
at
h
u
m
an
e
r
r
o
r
an
d
ex
h
a
u
s
tio
n
w
ill
d
im
in
is
h
th
e
p
r
ec
is
io
n
o
f
t
h
ese
m
eth
o
d
s
[
1
]
.
Stu
d
ies
in
v
es
tig
atin
g
th
e
u
s
e
o
f
im
ag
e
p
r
o
ce
s
s
in
g
tech
n
i
q
u
es
with
im
ag
es
o
f
p
lan
ts
h
av
e
b
ee
n
ca
r
r
ied
o
u
t
in
a
n
ef
f
o
r
t
to
o
v
er
co
m
e
th
e
s
h
o
r
tco
m
in
g
s
o
f
th
e
d
is
ea
s
e
d
e
tectio
n
m
eth
o
d
s
th
at
a
r
e
n
o
w
i
n
u
s
e.
I
n
1
9
8
3
,
an
au
to
m
ate
d
m
eth
o
d
u
s
in
g
v
id
eo
s
a
n
d
b
lack
-
a
n
d
-
wh
ite
im
ag
es
was
d
ev
el
o
p
ed
to
d
etec
t
p
lan
t
d
is
ea
s
es.
I
t
p
r
o
v
e
d
m
o
r
e
ac
cu
r
ate
th
an
v
is
u
al
c
h
ec
k
s
,
i
n
clu
d
in
g
in
m
aize
s
tr
ea
k
d
is
ea
s
e.
C
lass
ica
l
im
ag
e
p
r
o
ce
s
s
in
g
h
as
s
in
ce
b
ee
n
wid
ely
u
s
ed
f
o
r
d
ia
g
n
o
s
is
[
2
]
,
b
u
t
it
r
eq
u
ir
es
m
a
n
u
al
f
ea
tu
r
e
ex
tr
ac
tio
n
,
wh
ich
is
tim
e
-
co
n
s
u
m
in
g
an
d
p
r
o
n
e
to
b
ias.
T
r
ad
itio
n
al
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
h
av
e
b
ee
n
wid
e
ly
em
p
l
o
y
ed
b
y
th
e
s
cien
tific
co
m
m
u
n
ity
to
id
e
n
tify
p
lan
t
d
is
ea
s
es.
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
es
(
SVM)
wer
e
u
tili
ze
d
to
d
etec
t
to
m
ato
d
is
ea
s
es
[
3
]
.
T
o
id
en
t
if
y
to
m
ato
illn
ess
,
r
an
d
o
m
f
o
r
est
(
R
F)
alg
o
r
ith
m
s
wer
e
em
p
lo
y
ed
.
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
wer
e
u
s
ed
to
d
etec
t
s
o
y
b
ea
n
d
is
ea
s
es.
T
h
e
f
o
llo
win
g
lis
ts
a
n
u
m
b
e
r
o
f
ty
p
ical
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
f
o
r
class
if
y
in
g
an
d
i
d
en
tify
in
g
p
lan
t
d
is
ea
s
es.
B
ec
au
s
e
o
f
its
im
p
r
o
v
ed
p
r
o
ce
s
s
in
g
an
d
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
r
ev
iew
o
f m
o
d
ern
tech
n
iq
u
e
s
fo
r
p
la
n
t d
is
ea
s
e
id
en
tifi
ca
tio
n
a
n
d
w
ee
d
d
etec
tio
n
…
(
Mo
h
a
mma
d
N
a
s
ee
r
a
)
999
s
to
r
ag
e
ca
p
ab
ilit
ies
as
we
ll
a
s
its
ab
ilit
y
to
ef
f
ec
tiv
ely
h
an
d
le
lar
g
e
d
atasets
,
a
k
in
d
o
f
m
ac
h
in
e
lear
n
in
g
k
n
o
wn
as
d
ee
p
lear
n
i
n
g
h
as
g
ain
ed
p
o
p
u
lar
ity
as
a
to
o
l
f
o
r
s
ick
n
ess
d
iag
n
o
s
is
.
Sin
ce
th
e
2
0
1
2
I
m
ag
eNe
t
L
ar
g
e
Scale
Vis
u
al
R
ec
o
g
n
itio
n
C
h
allen
g
e
(
I
L
SVR
C
)
,
r
esear
ch
er
s
f
r
o
m
a
wid
e
r
an
g
e
o
f
d
is
cip
lin
es
h
av
e
b
ee
n
u
s
in
g
d
ee
p
lear
n
i
n
g
t
ec
h
n
iq
u
es
m
o
r
e
o
f
ten
.
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
ar
e
wid
el
y
em
p
lo
y
ed
in
d
ee
p
lear
n
i
n
g
ap
p
licatio
n
s
f
o
r
a
n
u
m
b
er
o
f
task
s
,
in
clu
d
in
g
o
b
ject
r
ec
o
g
n
itio
n
,
p
h
o
to
class
if
icatio
n
,
an
d
s
em
an
tic
s
eg
m
en
tatio
n
.
T
h
e
em
e
r
g
en
ce
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es,
p
ar
ticu
lar
ly
C
NN,
h
as
led
to
an
in
cr
ea
s
e
in
in
ter
est
i
n
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
r
esear
ch
[
4
]
.
Sin
ce
th
e
cr
ea
tio
n
o
f
th
e
Plan
tVillag
e
d
ataset
in
2
0
1
5
,
th
is
tr
en
d
h
as
b
ee
n
g
r
a
d
u
ally
g
r
o
win
g
.
Pr
o
jects
r
eq
u
ir
in
g
d
is
ea
s
e
d
iag
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[
5
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.
U
s
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e
te
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a
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o
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s
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T
h
e
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s
to
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ig
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co
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a
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to
m
a
n
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a
l
m
e
t
h
o
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s
[
6
]
.
T
h
e
p
r
esen
ce
o
f
wee
d
s
in
ag
r
icu
ltu
r
al
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d
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all.
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ar
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ap
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ef
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th
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ies.
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p
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d
is
tr
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tio
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en
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atter
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[
7
]
.
W
ee
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id
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tific
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y
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tem
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ar
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ilt
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q
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wo
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ar
tific
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(
AI
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eu
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(
ANN)
.
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ith
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p
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[
8
]
.
T
h
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[
9
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.
Fig
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u
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-
8
9
3
8
A
r
ev
iew
o
f m
o
d
ern
tech
n
iq
u
e
s
fo
r
p
la
n
t d
is
ea
s
e
id
en
tifi
ca
tio
n
a
n
d
w
ee
d
d
etec
tio
n
…
(
Mo
h
a
mma
d
N
a
s
ee
r
a
)
1001
T
h
e
to
p
ic
o
f
wee
d
id
en
tific
at
io
n
h
as
b
ee
n
ex
ten
s
iv
ely
s
tu
d
ied
u
s
in
g
a
v
ar
iety
o
f
m
ac
h
in
e
v
is
io
n
tech
n
iq
u
es
.
I
n
s
tu
d
y
[
1
0
]
d
ev
e
lo
p
ed
a
wee
d
-
cr
o
p
class
if
ier
u
s
in
g
f
u
zz
y
d
ec
is
io
n
-
m
ak
in
g
a
n
d
f
o
r
m
d
escr
ip
to
r
s
,
ac
h
iev
in
g
9
2
.
9
%
ac
cu
r
ac
y
o
n
6
6
f
ield
im
ag
es.
Du
r
in
g
th
e
in
itial
s
tag
es
o
f
th
e
g
r
o
wth
s
ea
s
o
n
,
c
r
o
p
s
t
y
p
ically
ex
h
ib
it
a
n
o
tab
le
ad
v
a
n
tag
e
o
v
er
wee
d
s
.
T
h
e
h
eig
h
t
attr
ib
u
te
was
u
tili
ze
d
to
estab
lis
h
a
m
eth
o
d
o
lo
g
y
f
o
r
d
if
f
er
en
tiatin
g
b
etwe
en
wee
d
s
an
d
cr
o
p
s
th
r
o
u
g
h
th
e
im
p
le
m
en
tatio
n
o
f
a
b
in
o
cu
lar
s
ter
e
o
v
is
io
n
s
y
s
tem
.
T
h
e
d
if
f
er
en
tiatio
n
b
etwe
en
wee
d
s
an
d
cr
o
p
s
was
ac
h
iev
ed
b
y
e
m
p
lo
y
in
g
a
h
eig
h
t
-
b
ased
s
eg
m
en
tatio
n
tech
n
iq
u
e
an
d
co
n
d
u
ctin
g
d
ep
th
d
im
en
s
io
n
an
aly
s
is
.
T
h
e
p
lan
t
s
p
ac
in
g
d
ata
was
em
p
lo
y
ed
to
d
if
f
er
en
tiate
b
etwe
en
th
e
cr
o
p
s
an
d
t
h
e
wee
d
s
,
s
p
ec
if
ica
lly
th
e
wee
d
s
th
at
wer
e
r
elativ
ely
taller
.
A
ty
p
ical
wee
d
d
etec
tio
n
s
y
s
t
em
co
m
p
r
is
es
f
o
u
r
ess
en
tial
p
r
o
ce
s
s
es,
im
ag
e
p
r
e
-
p
r
o
ce
s
s
in
g
,
f
ea
t
u
r
e
ex
tr
ac
tio
n
,
wee
d
id
e
n
tific
atio
n
an
d
class
if
icatio
n
,
an
d
im
a
g
e
co
llectio
n
[
1
1
]
.
T
h
e
s
u
cc
e
s
s
f
u
l
co
m
p
letio
n
o
f
th
ese
p
h
ases
h
as
b
ee
n
f
ac
ilit
ated
b
y
th
e
u
tili
za
tio
n
o
f
v
ar
i
o
u
s
s
tate
-
of
-
th
e
-
a
r
t
tech
n
o
lo
g
ies.
T
h
e
p
r
o
ce
s
s
o
f
s
o
r
tin
g
an
d
id
e
n
tify
in
g
wee
d
s
is
an
es
s
en
tial
s
tep
in
th
es
e
p
r
o
ce
d
u
r
es.
I
n
r
ec
e
n
t
tim
es,
th
er
e
h
as
b
ee
n
a
n
o
tab
le
r
is
e
in
th
e
u
tili
za
tio
n
o
f
em
b
ed
d
e
d
p
r
o
ce
s
s
o
r
s
an
d
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
o
lo
g
ie
s
f
o
r
th
e
p
u
r
p
o
s
e
o
f
au
to
n
o
m
o
u
s
wee
d
s
p
ec
ies id
en
tific
atio
n
.
T
h
e
p
r
im
ar
y
r
ea
s
o
n
f
o
r
th
is
ca
n
b
e
attr
ib
u
ted
to
t
h
e
p
r
o
g
r
ess
m
ad
e
in
co
m
p
u
ter
tec
h
n
o
lo
g
y
,
s
p
ec
if
ic
ally
in
th
e
d
o
m
ain
o
f
g
r
a
p
h
ics
p
r
o
ce
s
s
in
g
u
n
its
(
GPU)
[
1
2
]
.
A
s
ig
n
if
ican
t
s
u
b
s
et
o
f
m
ac
h
i
n
e
lear
n
in
g
is
d
ee
p
lear
n
in
g
.
D
ee
p
lear
n
in
g
tech
n
iq
u
es
o
f
f
er
s
ev
er
al
ad
v
an
tag
es
f
o
r
v
a
r
io
u
s
m
ac
h
in
e
lear
n
in
g
task
s
,
in
clu
d
in
g
o
b
ject
id
en
tific
atio
n
,
r
ec
o
g
n
itio
n
,
an
d
im
ag
e
class
if
icatio
n
.
Fo
r
th
e
p
u
r
p
o
s
es
o
f
th
is
s
tu
d
y
,
tr
ad
itio
n
al
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
w
ill
b
e
d
esig
n
ate
d
as
"m
ac
h
in
e
lear
n
in
g
.
"
T
h
e
a
p
p
licatio
n
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
f
o
r
th
e
d
if
f
e
r
en
tia
tio
n
o
f
wee
d
s
an
d
cr
o
p
s
p
r
esen
ts
ch
allen
g
es
d
u
e
to
th
e
in
h
er
en
t
s
im
ilar
ities
b
etwe
en
th
e
two
ca
teg
o
r
ies.
T
h
e
ad
v
a
n
ce
d
f
ea
tu
r
e
lear
n
in
g
ca
p
a
b
ilit
ies
o
f
d
ee
p
lear
n
in
g
alg
o
r
ith
m
s
o
f
f
e
r
a
v
iab
le
s
o
lu
tio
n
to
th
e
p
r
o
b
lem
.
W
h
ile
s
y
s
tem
atic
r
ev
iew
p
ap
er
s
r
em
ain
r
elativ
el
y
s
ca
r
ce
,
d
ee
p
lear
n
in
g
r
esear
ch
s
p
ec
if
ically
tar
g
etin
g
wee
d
d
etec
tio
n
h
as
s
ee
n
ex
p
o
n
e
n
tial g
r
o
wth
s
in
ce
2
0
2
0
,
d
r
iv
e
n
b
y
C
NN
ad
v
an
ce
m
e
n
ts
[
1
3
]
.
T
h
e
u
r
g
e
n
t
n
ee
d
to
s
o
lv
e
th
e
d
if
f
icu
lties
in
co
n
tem
p
o
r
ar
y
a
g
r
icu
ltu
r
e,
p
a
r
ticu
lar
ly
with
r
e
g
ar
d
to
th
e
id
en
tific
atio
n
o
f
wee
d
s
an
d
p
l
an
t
d
is
ea
s
es,
s
er
v
ed
as
th
e
im
p
etu
s
f
o
r
th
is
s
tu
d
y
.
Hig
h
ex
p
e
n
s
es
an
d
s
u
b
s
tan
tial
tim
e
co
m
m
itm
en
ts
ar
e
f
ea
tu
r
es
o
f
tr
ad
itio
n
al
m
eth
o
d
s
f
o
r
wee
d
m
an
ag
em
en
t
an
d
d
is
ea
s
e
d
iag
n
o
s
is
.
T
h
ese
tech
n
iq
u
es
ar
e
also
p
r
o
n
e
to
h
u
m
an
er
r
o
r
,
wh
ich
m
a
y
p
r
o
v
id
e
less
-
th
an
-
id
ea
l
r
esu
lts
th
at
h
av
e
a
n
eg
ativ
e
im
p
ac
t o
n
cr
o
p
o
u
tp
u
t a
n
d
q
u
a
lity
.
T
h
e
d
e
v
elo
p
m
e
n
t
o
f
d
ee
p
lea
r
n
in
g
an
d
m
ac
h
in
e
lear
n
i
n
g
t
ec
h
n
o
lo
g
ies
o
f
f
e
r
s
a
s
in
g
u
lar
ch
an
ce
to
im
p
r
o
v
e
e
x
is
tin
g
p
r
o
ce
s
s
es
b
y
p
u
ttin
g
au
to
m
ated
,
ac
cu
r
ate,
an
d
s
ca
lab
le
s
o
lu
tio
n
s
in
to
p
la
ce
.
T
h
e
p
u
r
p
o
s
e
o
f
th
is
r
ev
iew
is
to
ex
am
in
e
an
d
co
m
p
ile
th
e
m
o
s
t
r
ec
en
t
d
ev
el
o
p
m
en
ts
in
d
ee
p
lear
n
i
n
g
m
et
h
o
d
s
f
o
r
wee
d
an
d
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
.
I
t
d
r
aws
atten
tio
n
to
h
o
w
th
ese
tech
n
iq
u
es
m
ig
h
t
im
p
r
o
v
e
th
e
s
u
s
tain
ab
ilit
y
o
f
ag
r
icu
ltu
r
al
o
p
er
atio
n
s
an
d
le
s
s
en
d
ep
en
d
e
n
cy
o
n
d
a
n
g
er
o
u
s
ch
em
icals
lik
e
p
esti
cid
es.
B
y
r
ev
iewin
g
an
d
an
aly
zin
g
th
e
c
u
r
r
e
n
t
s
tate
o
f
th
e
a
r
t,
th
is
p
ap
er
s
ee
k
s
to
i
d
en
tify
r
esear
ch
g
ap
s
a
n
d
p
r
o
p
o
s
e
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
wo
r
k
,
u
ltima
tely
co
n
tr
ib
u
tin
g
to
t
h
e
d
ev
el
o
p
m
en
t
o
f
m
o
r
e
ef
f
icien
t,
p
r
ec
is
e,
a
n
d
en
v
i
r
o
n
m
e
n
tally
f
r
ien
d
ly
a
g
r
icu
ltu
r
al
tec
h
n
o
lo
g
ies.
I
n
th
e
co
n
tex
t
o
f
ag
r
ic
u
ltu
r
a
l
tech
n
o
lo
g
y
,
p
la
n
t
d
is
ea
s
e
i
d
en
tific
atio
n
an
d
wee
d
d
etec
tio
n
s
h
ar
e
s
ev
er
al
in
ter
r
elate
d
ch
allen
g
e
s
th
at
in
f
lu
e
n
ce
th
ei
r
ef
f
ec
tiv
en
ess
wh
en
u
s
in
g
d
ee
p
lear
n
i
n
g
tech
n
iq
u
es.
B
o
th
f
ield
s
r
eq
u
ir
e
ex
ten
s
iv
e
d
ata
co
llectio
n
ef
f
o
r
ts
,
o
f
te
n
f
ac
in
g
is
s
u
es
s
u
ch
as
lim
ited
av
ail
ab
ilit
y
o
f
a
n
n
o
tated
d
atasets
,
v
ar
iab
ilit
y
in
s
p
ec
ies
ap
p
ea
r
an
ce
s
,
an
d
th
e
im
p
ac
t
o
f
en
v
ir
o
n
m
en
tal
c
o
n
d
itio
n
s
o
n
d
ata
q
u
ality
.
T
h
e
ch
allen
g
es
o
f
class
im
b
alan
ce
,
wh
er
e
ce
r
tain
d
is
ea
s
es
o
r
we
ed
s
p
ec
ies
ar
e
u
n
d
er
r
ep
r
esen
t
ed
,
ar
e
p
r
ev
alen
t
in
b
o
th
ar
ea
s
,
co
m
p
licatin
g
m
o
d
el
tr
ain
in
g
an
d
lea
d
in
g
to
p
o
te
n
tial
b
iases
.
Gen
er
aliza
tio
n
ac
r
o
s
s
d
if
f
er
en
t
cr
o
p
s
,
r
eg
io
n
s
,
an
d
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
s
is
an
o
th
er
c
o
m
m
o
n
h
u
r
d
le,
as
m
o
d
els
tr
ain
ed
o
n
o
n
e
d
ataset
m
ay
n
o
t
p
er
f
o
r
m
well
wh
en
a
p
p
lied
to
n
ew
s
ce
n
ar
io
s
.
Fu
r
th
e
r
m
o
r
e,
b
o
th
p
lan
t
d
is
ea
s
e
id
en
tific
atio
n
an
d
wee
d
d
etec
tio
n
en
co
u
n
ter
d
if
f
icu
lties
in
m
o
d
el
in
ter
p
r
etab
ilit
y
,
wh
er
e
th
e
co
m
p
lex
it
y
o
f
d
ee
p
lear
n
in
g
m
o
d
els
m
ak
es it c
h
allen
g
in
g
t
o
ex
p
lain
d
ec
is
io
n
s
,
r
ed
u
cin
g
tr
u
s
t a
m
o
n
g
en
d
-
u
s
er
s
lik
e
f
a
r
m
er
s
.
Fin
ally
,
th
e
d
ep
lo
y
m
e
n
t
o
f
t
h
ese
m
o
d
els
in
r
ea
l
-
wo
r
ld
a
g
r
icu
ltu
r
al
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ettin
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s
in
v
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lv
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co
m
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n
g
o
b
s
tacle
s
r
elate
d
to
co
m
p
u
tatio
n
al
r
eq
u
ir
em
en
ts
,
s
ca
lab
ilit
y
,
an
d
in
teg
r
atio
n
with
e
x
is
tin
g
f
ar
m
in
g
p
r
ac
tices,
all
wh
ile
ad
h
er
in
g
to
r
e
g
u
lato
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y
s
tan
d
ar
d
s
an
d
co
n
s
id
er
i
n
g
et
h
ical
im
p
licatio
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s
.
T
h
ese
in
ter
r
elate
d
ch
allen
g
es
h
ig
h
lig
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t
th
e
n
ee
d
f
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c
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m
p
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ess
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o
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d
o
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s
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h
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ce
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ic
u
ltu
r
al
p
r
o
d
u
ctiv
ity
an
d
s
u
s
tain
ab
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.
T
ab
le
1
(
in
Ap
p
en
d
ix
)
s
u
m
m
ar
izes
an
d
co
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tr
asts
th
e
ch
allen
g
es
en
co
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n
ter
e
d
in
b
o
th
p
lan
t d
is
ea
s
e
id
en
tific
a
tio
n
an
d
wee
d
d
etec
tio
n
ac
r
o
s
s
m
u
ltip
le
asp
ec
ts
.
T
h
e
co
n
t
r
ib
u
tio
n
s
o
f
th
is
ar
e:
i)
C
o
m
p
r
eh
en
s
iv
e
r
ev
iew
o
f
s
tate
-
of
-
th
e
-
ar
t
tech
n
iq
u
es:
th
is
s
u
r
v
ey
p
r
o
v
id
es
a
d
etailed
ex
am
in
atio
n
o
f
th
e
latest
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lea
r
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in
g
ap
p
r
o
ac
h
es
f
o
r
p
lan
t
d
is
ea
s
e
id
en
tific
ati
o
n
an
d
wee
d
d
etec
tio
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,
co
v
er
in
g
v
ar
io
u
s
m
eth
o
d
s
,
d
atasets
,
an
d
e
v
alu
atio
n
m
etr
ics
u
s
ed
in
r
e
ce
n
t
r
esear
ch
.
I
t
o
f
f
er
s
a
co
n
s
o
lid
ated
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eso
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r
ce
f
o
r
r
ese
ar
ch
er
s
an
d
p
r
ac
titi
o
n
er
s
in
t
h
e
f
ield
.
ii)
I
d
en
tific
atio
n
o
f
c
h
allen
g
es
a
n
d
r
esear
c
h
g
a
p
s
:
b
y
a
n
aly
zin
g
th
e
c
u
r
r
e
n
t
liter
atu
r
e,
th
is
p
ap
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id
e
n
tifie
s
k
ey
ch
allen
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es
s
u
ch
as
d
ata
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ca
r
city
,
m
o
d
el
g
en
e
r
aliza
tio
n
,
an
d
co
m
p
u
tatio
n
al
r
eq
u
ir
e
m
en
ts
.
I
t
also
h
ig
h
lig
h
ts
s
p
ec
if
ic
r
esear
ch
g
ap
s
,
p
ar
ticu
lar
ly
in
t
h
e
in
teg
r
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o
f
m
u
lti
-
m
o
d
al
d
ata
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d
th
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d
f
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r
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tim
e,
s
ca
lab
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,
a
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d
g
u
i
d
in
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f
u
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r
e
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esear
ch
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ts
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Evaluation Warning : The document was created with Spire.PDF for Python.
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iii)
Pro
p
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s
ed
d
i
r
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
:
th
e
s
u
r
v
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ests
p
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ten
tial
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tio
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ad
v
an
cin
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f
ield
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a
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itio
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lear
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tech
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2.
RE
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D
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h
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m
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(
T
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m
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.
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s
.
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b
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d
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s
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s
te
d
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w
as
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d
,
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q
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le
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f
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m
a
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p
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d
u
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b
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e
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s
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l
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s
.
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d
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ay
b
e
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b
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tify
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p
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leaf
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is
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es
[
1
4
]
,
[
1
5
]
.
T
h
e
web
s
ite
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f
f
er
s
th
e
ab
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t
o
d
o
wn
lo
ad
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ag
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f
r
o
m
th
e
Pla
n
tVillag
e
d
ataset.
C
NN
wer
e
u
s
ed
to
class
if
y
p
lan
t
leaf
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ac
co
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an
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cr
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ets,
th
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tr
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0
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wh
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test
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T
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test
in
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tewo
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.
I
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s
tu
d
y
[
1
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]
an
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tech
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ataset.
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tech
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ac
cu
r
ac
y
.
Du
b
e
et
a
l
.
[
1
7
]
h
av
e
in
tr
o
d
u
c
ed
a
C
NN
-
b
ased
m
o
d
el
f
o
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th
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d
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d
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ato
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is
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s
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h
is
m
o
d
el
u
s
es
a
p
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licly
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ataset
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l
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n
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ativ
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les
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at
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ates
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to
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s
es.
Yu
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u
f
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d
Nis
war
[
1
8
]
u
s
e
th
e
Plan
tVillag
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d
ataset
was
to
d
em
o
n
s
tr
ate
class
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icatio
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ass
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ir
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d
f
o
u
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ial
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s
s
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s
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m
a
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th
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h
ea
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k
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la
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ey
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lev
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-
o
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r
r
e
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ce
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atr
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GL
C
Ms)
,
SVMs,
an
d
C
N
Ns
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e
am
o
n
g
th
e
m
ac
h
in
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lear
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i
n
g
tech
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iq
u
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s
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u
ild
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r
ed
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d
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AI
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ce
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f
o
r
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t
ask
s
,
b
ac
k
p
r
o
p
ag
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tech
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iq
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es
in
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e
also
p
r
o
g
r
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ed
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n
o
r
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er
t
o
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tect
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is
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s
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ea
l
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tim
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p
h
o
to
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u
r
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n
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a
K
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m
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s
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lu
s
ter
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g
o
p
er
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am
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ed
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r
to
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at
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lan
ts
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r
r
esp
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n
d
i
n
g
o
v
e
r
all
ac
cu
r
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was
9
6
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9
4
%,
9
5
%,
an
d
9
7
%.
Fo
r
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ile
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s
9
8
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T
h
e
f
o
llo
win
g
ar
e
th
e
o
u
tco
m
es p
r
o
d
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ce
d
b
y
th
e
s
u
g
g
ested
p
r
o
ce
s
s
.
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h
is
s
tu
d
y
ev
alu
ated
m
u
lti
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class
class
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p
r
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lem
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s
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g
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-
m
ea
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r
e,
p
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io
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d
r
e
ca
ll
m
ea
s
u
r
es.
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h
e
d
ataset
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s
ed
f
o
r
th
ese
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esu
lts
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clu
d
es
a
s
in
g
le
s
y
m
p
to
m
p
o
o
l
f
o
r
ea
ch
class
.
An
im
p
r
o
v
ed
C
NN
ap
p
r
o
ac
h
is
s
u
g
g
ested
f
o
r
th
e
d
etec
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o
f
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illn
ess
es.
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p
n
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r
al
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etwo
r
k
s
(
DNNs)
ar
e
r
em
a
r
k
ab
ly
ad
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p
t
in
p
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r
e
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teg
o
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izatio
n
task
s
.
T
h
e
ap
p
licatio
n
o
f
DNNs
f
o
r
th
e
c
ateg
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r
izatio
n
o
f
p
lan
t
d
is
ea
s
e
p
h
o
to
s
is
d
em
o
n
s
tr
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in
t
h
is
s
tu
d
y
.
T
h
is
ar
ticle
ass
es
s
es
th
e
p
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ec
is
io
n
o
f
ex
is
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g
m
eth
o
d
s
.
T
L
,
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NN+
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L
,
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an
d
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NN
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ar
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am
o
n
g
th
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m
eth
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s
ex
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in
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d
; th
eir
r
esp
ec
tiv
e
ac
cu
r
ac
ies ar
e
8
0
%,
8
5
%
,
9
0
%,
an
d
9
5
%.
C
o
m
p
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R
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LR
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C
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~
9
7
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C
NN
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a
c
c
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ac
y
[
1
9
]
.
P
h
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r
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p
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b
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k
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8
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A
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2
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.
T
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f
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l
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[
2
2
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d
escr
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p
ix
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b
ased
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ased
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a
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c
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p
[
1
3
]
Y
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seri
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s
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M
a
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[
4
]
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r
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9
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d
sc
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n
a
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o
s.
[
2
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C
N
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L
m
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d
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l
)
+
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V
p
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[
2
1
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[
1
6
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TL
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[
1
1
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C
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R
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[
1
2
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R
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b
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ma
c
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F
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c
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r
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AI
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% m
o
d
el
ac
cu
r
ac
y
o
b
tai
n
ed
b
y
u
s
in
g
K
-
m
ea
n
s
as a
p
r
e
-
tr
ain
in
g
m
et
h
o
d
o
lo
g
y
.
Du
e
to
th
eir
s
tr
ik
in
g
s
im
ilar
ities
in
ch
ar
ac
ter
is
tics
,
it
ca
n
b
e
d
if
f
icu
lt
to
d
is
tin
g
u
is
h
b
etw
ee
n
wee
d
s
an
d
cr
o
p
s
.
An
d
r
ea
et
a
l
.
[
2
2
]
i
n
v
esti
g
ated
two
m
eth
o
d
s
th
at
m
ak
e
u
s
e
o
f
f
o
r
m
tr
aits
to
cr
ea
te
a
wee
d
d
etec
tio
n
s
y
s
tem
in
o
r
d
er
to
ad
d
r
ess
th
is
p
r
o
b
lem
.
I
n
o
r
d
er
to
c
r
ea
te
a
p
atter
n
-
b
ased
s
y
s
tem
f
o
r
wee
d
d
etec
tio
n
,
th
e
s
tu
d
y
ap
p
lied
ANN
an
d
SVM.
Sh
ir
az
Un
iv
er
s
ity
'
s
s
u
g
ar
b
ee
t
f
ield
s
wer
e
p
h
o
to
g
r
ap
h
ed
b
y
h
an
d
in
o
r
d
er
to
co
m
p
ile
th
e
d
ataset.
T
o
e
v
alu
ate
th
e
p
lan
t'
s
m
o
r
p
h
o
l
o
g
ical
t
r
aits
,
p
ictu
r
es
o
f
it
we
r
e
tak
e
n
wh
en
it
was
at
t
h
e
f
o
u
r
-
lea
f
d
ev
elo
p
m
en
t
s
tag
e.
T
h
e
r
eso
lu
tio
n
o
f
th
e
p
ictu
r
es
was
9
6
0
b
y
1
,
2
8
0
p
ix
els.
T
h
e
6
0
0
im
ag
es
in
th
e
co
llectio
n
ar
e
ar
r
a
n
g
ed
in
to
g
r
o
u
p
s
o
f
1
2
0
im
a
g
es
ap
iece
.
T
h
e
R
GB
f
o
r
m
at
was
u
s
ed
to
ca
p
tu
r
e
th
e
p
h
o
to
g
r
ap
h
s
.
An
aly
zi
n
g
p
h
o
t
o
g
r
ap
h
s
to
id
en
tify
th
eir
g
r
ee
n
f
ea
tu
r
es
is
p
ar
t
o
f
th
e
p
r
e
-
p
r
o
ce
s
s
in
g
s
tep
.
T
o
m
ak
e
f
ea
tu
r
e
e
x
tr
ac
tio
n
ea
s
ier
,
th
e
R
GB
p
ictu
r
es
in
th
e
d
ataset
ar
e
co
n
v
er
ted
to
g
r
ey
s
ca
l
e
f
o
r
m
at.
Fo
r
p
lan
t
an
aly
s
is
,
th
e
SVM
an
d
ANN
class
if
icatio
n
m
eth
o
d
s
wer
e
ap
p
lied
an
d
ass
ess
ed
.
W
ith
h
id
d
en
lay
er
s
,
th
e
ANN
f
u
n
ctio
n
s
as a
f
ee
d
-
f
o
r
war
d
ar
ch
itectu
r
e.
Data
was tr
an
s
f
er
r
ed
b
etwe
en
th
e
h
id
d
en
lay
er
s
u
s
in
g
th
e
t
an
g
en
t
s
ig
m
o
id
an
d
lo
g
ar
ith
m
s
ig
m
o
id
f
u
n
ctio
n
s
.
W
h
ile
tex
tu
r
e
f
ea
tu
r
es
ar
e
in
co
r
p
o
r
ated
in
t
o
th
e
ANN
to
im
p
r
o
v
e
t
h
e
d
is
cr
im
in
atin
g
p
r
o
ce
s
s
,
p
r
in
cip
al
c
o
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
is
u
s
ed
to
lo
wer
th
e
d
im
en
s
io
n
a
lity
o
f
th
e
in
p
u
t
d
ata
.
A
SVM
is
u
s
ed
to
class
if
y
th
e
ty
p
es
o
f
p
lan
ts
.
T
h
e
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
a
n
d
R
2
v
alu
es
ar
e
e
v
alu
ated
i
n
o
r
d
er
to
d
eter
m
in
e
th
e
ac
cu
r
ac
y
o
f
th
e
SVM.
T
o
ass
es
s
th
e
ANN
p
er
f
o
r
m
an
ce
,
th
e
co
n
f
u
s
io
n
m
atr
ix
was
co
m
p
u
ted
.
SVM
r
ea
ch
an
ac
c
u
r
ac
y
o
f
8
8
%,
wh
il
e
ANN
attain
an
ac
cu
r
ac
y
o
f
8
6
%.
Acc
o
r
d
in
g
to
th
e
r
esu
lts
,
th
e
SVM
o
u
tp
er
f
o
r
m
s
an
ANN
in
clas
s
if
icatio
n
wh
en
a
s
h
ap
e
f
e
atu
r
e
is
u
s
ed
f
o
r
m
o
d
el
tr
ain
in
g
.
Fo
r
ef
f
icien
t
a
r
ea
-
s
p
ec
if
ic
we
ed
co
n
tr
o
l
in
a
g
r
icu
ltu
r
e,
we
ed
id
en
tific
atio
n
an
d
ca
teg
o
r
izatio
n
ar
e
ess
en
tial.
B
is
wa
s
an
d
Asl
ek
ar
[
2
3
]
u
s
ed
s
p
atial
r
eso
lu
tio
n
an
d
s
p
ec
tr
al
b
an
d
s
to
s
t
u
d
y
wee
d
d
etec
tio
n
s
y
s
tem
s
.
B
ec
au
s
e
h
er
b
icid
es h
av
e
d
etr
im
en
tal
ef
f
ec
ts
o
n
cr
o
p
h
ea
lth
an
d
h
u
m
a
n
h
ea
lth
,
th
e
au
th
o
r
'
s
m
ain
g
o
al
is
to
d
ec
r
ea
s
e
th
eir
u
s
e
in
ag
r
icu
ltu
r
al
co
n
te
x
ts
.
T
h
e
C
NN
an
d
h
is
to
g
r
am
o
f
o
r
ie
n
ted
g
r
ad
ien
t
(
HOG
)
ap
p
r
o
ac
h
es
ar
e
co
m
p
ar
ed
an
d
ev
alu
ated
in
o
r
d
er
to
d
eter
m
in
e
th
e
ef
f
ec
tiv
en
ess
o
f
wee
d
d
etec
tio
n
.
T
ab
le
3
co
m
p
iles
d
if
f
er
e
n
t
wee
d
d
ete
ctio
n
m
eth
o
d
s
,
co
m
p
ar
i
n
g
th
eir
p
er
f
o
r
m
an
ce
a
n
d
id
e
n
tify
in
g
ar
ea
s
n
ee
d
in
g
f
u
r
th
er
i
n
v
esti
g
atio
n
.
W
u
et
a
l
.
[
2
4
]
s
u
g
g
ested
to
e
n
ab
le
y
ield
ca
lcu
latio
n
an
d
au
to
n
o
m
o
u
s
h
er
b
icid
e
s
p
r
a
y
in
g
,
an
im
a
g
e
p
r
o
ce
s
s
in
g
s
y
s
tem
was
cr
ea
ted
to
d
is
tin
g
u
is
h
b
etwe
en
cr
o
p
s
an
d
wee
d
s
b
ased
o
n
tex
tu
r
e
an
d
s
ize
f
ea
tu
r
es.
Fo
r
cr
o
p
d
etec
tio
n
,
f
iv
e
tex
tu
r
e
attr
ib
u
tes
ar
e
u
s
ed
.
E
n
er
g
y
,
en
tr
o
p
y
,
in
er
tia,
lo
ca
l
h
o
m
o
g
e
n
eity
,
an
d
co
n
t
r
ast
ar
e
th
e
f
iv
e
attr
ib
u
tes.
Ad
d
itio
n
ally
,
tr
aits
b
ased
o
n
m
o
r
p
h
o
lo
g
ical
s
ize
ar
e
u
s
ed
in
cr
o
p
an
d
wee
d
id
en
tific
atio
n
.
A
th
o
r
o
u
g
h
ass
ess
m
en
t
o
f
all
r
esu
lt
s
led
to
th
e
estab
lis
h
m
en
t
o
f
th
e
m
ajo
r
it
y
s
elec
tio
n
f
o
r
cr
o
p
an
d
wee
d
d
etec
tio
n
.
T
o
ex
tr
a
ct
a
ce
ll
f
r
o
m
an
im
a
g
e,
im
ag
e
s
eg
m
en
tatio
n
u
s
es
a
r
a
n
g
e
o
f
im
ag
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es.
T
h
e
d
ec
is
io
n
-
m
ak
in
g
m
ec
h
an
is
m
d
eter
m
in
es
w
h
ich
ce
lls
will
b
e
s
p
r
ay
ed
.
B
y
f
ig
u
r
in
g
o
u
t
th
e
co
o
r
d
in
ates
r
eq
u
ir
e
d
f
o
r
s
ele
ctiv
e
h
er
b
icid
e
tr
ea
tm
en
t,
a
C
ar
tesi
an
r
o
b
o
t
m
a
n
ip
u
lato
r
is
cr
ea
ted
to
lo
ca
te
wee
d
s
in
an
ac
tu
al
f
ield
.
TL
im
p
r
o
v
es
wee
d
d
etec
tio
n
ac
cu
r
ac
y
wh
ile
lo
wer
in
g
th
e
a
m
o
u
n
t
o
f
d
ata
an
d
co
m
p
u
tin
g
r
eso
u
r
ce
s
n
ee
d
ed
f
o
r
DNN
tr
ain
i
n
g
.
T
h
i
s
is
esp
ec
ially
im
p
o
r
tan
t
b
ec
a
u
s
e
th
e
s
win
t
r
an
s
f
o
r
m
e
r
n
et
wo
r
k
n
ee
d
s
a
lo
t
o
f
tr
ain
in
g
d
ata.
T
h
e
in
p
u
t
f
o
r
a
two
-
s
tag
e
TL
ap
p
r
o
ac
h
is
a
s
win
tr
an
s
f
o
r
m
er
n
etwo
r
k
th
a
t
h
as
alr
ea
d
y
b
ee
n
tr
ain
ed
o
n
t
h
e
I
m
ag
eNe
t
d
ataset.
I
n
o
r
d
er
to
im
p
r
o
v
e
r
ec
o
g
n
itio
n
ac
cu
r
ac
y
in
wee
d
r
ec
o
g
n
itio
n
task
s
an
d
f
u
r
th
er
r
ed
u
ce
th
e
r
e
q
u
ir
em
e
n
t
f
o
r
tr
ain
in
g
d
ata
q
u
an
tity
,
t
h
is
m
eth
o
d
was
s
u
g
g
ested
f
o
r
n
etwo
r
k
t
r
ain
in
g
in
th
is
s
tu
d
y
.
All
o
f
th
e
s
win
tr
an
s
f
o
r
m
e
r
n
etwo
r
k
'
s
p
ar
am
eter
s
—
asi
d
e
f
r
o
m
th
o
s
e
in
t
h
e
last
co
m
p
letely
co
n
n
ec
ted
la
y
er
-
ar
e
o
b
tain
ed
b
y
th
e
f
ea
t
u
r
e
ex
tr
a
ctio
n
n
etwo
r
k
.
T
h
e
f
ea
tu
r
e
e
x
tr
ac
tio
n
n
etwo
r
k
is
th
e
n
ad
ju
s
ted
u
s
in
g
th
e
p
lan
t
s
ee
d
lin
g
s
d
ataset
to
cr
ea
te
a
task
-
s
p
ec
if
ic
p
r
e
-
tr
ain
ed
n
etwo
r
k
th
at
im
p
r
o
v
es
r
elev
an
ce
f
o
r
wee
d
i
d
en
tific
at
io
n
task
s
.
Usi
n
g
o
u
r
m
aize
/wee
d
f
ield
im
ag
e
(
MWF
I
)
d
ata
s
et,
th
e
task
-
r
elate
d
p
r
e
-
tr
ain
ed
n
etwo
r
k
is
f
in
e
-
tu
n
ed
to
p
r
o
d
u
ce
th
e
f
in
al
wee
d
r
ec
o
g
n
itio
n
n
etwo
r
k
[
2
5
]
.
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
r
ev
iew
o
f m
o
d
ern
tech
n
iq
u
e
s
fo
r
p
la
n
t d
is
ea
s
e
id
en
tifi
ca
tio
n
a
n
d
w
ee
d
d
etec
tio
n
…
(
Mo
h
a
mma
d
N
a
s
ee
r
a
)
1005
T
ab
le
3
.
C
o
m
p
a
r
ativ
e
an
aly
s
is
o
f
wee
d
d
etec
tio
n
ap
p
r
o
ac
h
es
M
e
t
h
o
d
A
d
v
a
n
t
a
g
e
s
D
i
sad
v
a
n
t
a
g
e
s
R
e
se
a
r
c
h
g
a
p
P
i
x
e
l
-
b
a
se
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
u
si
n
g
RF
w
i
t
h
1
0
d
e
c
i
s
i
o
n
t
r
e
e
s.
Ef
f
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c
t
i
v
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n
h
a
n
d
l
i
n
g
o
c
c
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u
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o
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n
d
o
v
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r
l
a
p
p
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o
f
p
l
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n
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s.
Li
mi
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d
t
o
p
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x
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b
a
se
d
c
l
a
ss
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f
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c
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w
h
i
c
h
ma
y
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ss c
o
n
t
e
x
t
u
a
l
i
n
f
o
r
m
a
t
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o
n
.
I
t
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s
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d
v
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b
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s:
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t
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p
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t
.
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s
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r
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.
3.
CO
NCLU
SI
O
N
T
h
e
s
u
r
v
ey
co
n
cl
u
d
es
b
y
h
i
g
h
lig
h
tin
g
th
e
g
r
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win
g
s
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n
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f
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ce
o
f
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
in
g
tech
n
iq
u
es
in
tr
a
n
s
f
o
r
m
in
g
wee
d
d
etec
tio
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an
d
p
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t
d
is
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s
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d
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n
o
s
is
in
p
r
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is
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n
ag
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ltu
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e.
T
h
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tech
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o
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ies
p
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v
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e
f
ascin
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