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(
C
NNs)
ar
e
co
n
s
id
er
ed
o
n
e
ty
p
e
o
f
d
ee
p
lear
n
in
g
m
o
d
el
th
at
h
as
p
r
o
v
en
to
b
e
v
er
y
s
u
cc
es
s
f
u
l
in
i
m
a
g
e
-
b
ase
d
task
s
ai
m
ed
at
p
est
id
en
tif
i
ca
tio
n
.
E
q
u
all
y
,
th
e
YO
L
O
s
y
s
te
m
i
m
p
r
o
v
e
s
th
e
d
etec
tio
n
ca
p
ab
ilit
y
o
f
p
ests
an
d
d
is
ea
s
es,
b
y
ad
d
in
g
ad
ap
tiv
e
s
p
atial
f
ea
t
u
r
e
f
u
s
io
n
,
w
h
i
ch
b
o
o
s
ts
d
etec
tio
n
ef
f
ec
tiv
e
n
e
s
s
w
it
h
o
u
t
au
g
m
en
t
in
g
t
h
e
co
m
p
u
tatio
n
al
ex
p
en
s
es
[
2
]
-
[
5
]
.
Su
ch
s
o
p
h
is
ticated
ar
ch
itect
u
r
es
as
th
e
d
ilated
m
u
lti
-
s
ca
le
atten
tio
n
U
-
Net
h
a
v
e
b
ee
n
co
n
s
tr
u
cted
to
o
v
er
co
m
e
p
r
o
b
lem
s
th
at
e
x
i
s
t
in
th
e
d
etec
tio
n
o
f
p
ests
o
f
d
if
f
er
en
t
s
h
ap
es
an
d
s
izes
o
n
co
m
p
lex
b
ac
k
g
r
o
u
n
d
s
an
d
h
av
e
p
r
o
v
ed
to
b
e
ef
f
ec
tiv
e
in
t
h
e
f
ield
.
Fu
r
t
h
er
m
o
r
e,
C
NN
-
b
ased
m
o
d
els
w
it
h
atte
n
tio
n
m
ec
h
a
n
i
s
m
s
a
n
d
r
ec
u
r
r
en
t
u
n
its
,
in
c
lu
d
in
g
t
h
e
co
n
v
o
lu
tio
n
al
s
lice
-
a
tten
tio
n
-
b
ased
g
ated
r
e
cu
r
r
en
t
u
n
it
,
h
av
e
d
e
m
o
n
s
tr
at
ed
g
r
ea
t
ac
cu
r
ac
y
i
n
th
e
p
est
s
eg
m
en
tatio
n
a
n
d
class
i
f
icatio
n
tas
k
s
[
6
]
-
[
9
]
.
I
n
th
is
ca
s
e,
th
e
m
o
s
t
e
f
f
ec
tiv
e
m
eth
o
d
o
lo
g
ies
ar
e
E
f
f
icie
n
tNet
,
YOL
O
v
ar
iatio
n
s
,
an
d
C
NN
s
to
u
s
e
as
i
n
telli
g
e
n
t
p
est
d
etec
tio
n
an
d
u
s
e
cla
s
s
i
f
i
ca
tio
n
s
y
s
te
m
s
.
YO
L
O
v
8
en
h
an
ce
s
t
h
e
d
etec
tio
n
s
p
ee
d
an
d
th
e
d
etec
tio
n
ac
c
u
r
ac
y
,
w
h
er
ea
s
C
N
Ns
ex
tr
a
ct
r
elev
an
t
f
ea
t
u
r
es
o
f
i
m
a
g
es.
Scalab
le
C
NN
E
f
f
icien
tNet
-
B
7
u
s
es
d
ep
th
,
b
r
ea
d
th
,
an
d
r
eso
lu
tio
n
to
o
p
ti
m
ize
m
i
n
u
s
cu
le
s
ca
le
ca
teg
o
r
izatio
n
.
A
ll
t
h
ese
f
ac
to
r
s
co
m
b
i
n
e
to
ass
i
s
t
i
n
g
iv
in
g
a
v
iab
le
s
o
lu
tio
n
to
m
o
d
er
n
p
est
co
n
tr
o
l
i
n
ag
r
i
cu
lt
u
r
e
th
r
o
u
g
h
th
e
in
te
g
r
atio
n
o
f
r
ea
l
-
ti
m
e
ac
ce
s
s
ib
ilit
y
i
n
clo
u
d
s
,
p
r
ec
is
e
clas
s
i
f
icatio
n
,
an
d
r
ap
id
id
en
tif
icati
o
n
.
C
o
m
b
i
n
in
g
d
ee
p
lear
n
in
g
w
it
h
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
d
e
v
ices,
s
u
ch
s
y
s
te
m
s
h
e
lp
to
c
o
llect
an
d
p
r
o
ce
s
s
d
ata
in
r
ea
l
-
ti
m
e
,
an
d
co
n
d
u
ct
in
ter
v
en
tio
n
s
in
ti
m
e
,
an
d
eli
m
in
a
te
th
e
n
ee
d
to
u
s
e
m
a
n
u
al
lab
o
r
.
T
h
e
cu
r
r
en
t
p
r
o
j
ec
t
w
i
ll
f
o
c
u
s
o
n
th
e
cr
ea
tio
n
o
f
a
d
etailed
p
es
t
d
etec
tio
n
s
y
s
te
m
t
h
at
w
ill
u
tili
ze
th
e
late
s
t
D
L
m
o
d
el
s
an
d
I
o
T
tech
n
o
lo
g
ies
to
g
iv
e
p
r
ec
is
e,
e
f
f
ec
t
iv
e,
a
n
d
s
ca
lab
le
s
o
lu
tio
n
s
to
th
e
p
r
o
b
lem
o
f
m
o
d
er
n
ag
r
icu
l
tu
r
e
[
1
0
]
-
[
1
4
]
.
T
h
e
I
o
T
is
a
n
et
w
o
r
k
o
f
m
u
ltip
le
in
ter
co
n
n
ec
te
d
d
ev
ices
t
h
at
ar
e
ca
p
ab
le
o
f
co
llectin
g
,
tr
an
s
m
itti
n
g
,
an
d
s
to
r
in
g
in
f
o
r
m
at
io
n
.
T
h
e
I
o
T
is
ch
an
g
i
n
g
th
e
w
a
y
a
g
r
icu
l
tu
r
al
ac
t
i
v
itie
s
ar
e
d
o
n
e
b
y
en
ab
lin
g
a
u
to
m
ated
d
ec
is
io
n
-
m
ak
in
g
,
d
ata
co
llectio
n
,
a
n
d
r
ea
l
-
ti
m
e
m
o
n
ito
r
i
n
g
.
R
e
m
o
te
s
en
s
in
g
o
f
p
e
s
ts
w
i
ll
ass
is
t f
ar
m
er
s
i
n
th
e
d
is
co
v
er
y
an
d
m
an
a
g
e
m
e
n
t o
f
p
est
s
.
T
h
e
co
m
b
in
atio
n
o
f
th
e
I
o
T
d
ev
ices
an
d
o
th
er
tech
n
o
lo
g
ies
in
th
e
ag
r
ic
u
lt
u
r
al
in
d
u
s
tr
y
ca
n
h
elp
th
e
m
o
d
er
n
f
ar
m
i
n
g
p
r
ac
tices
to
b
e
m
o
r
e
p
r
o
d
u
ctiv
e,
h
a
v
e
f
e
w
er
m
a
n
u
al
in
ter
v
e
n
tio
n
s
,
p
r
o
ac
tiv
e
i
n
ter
v
e
n
tio
n
,
an
d
b
ased
d
ec
is
io
n
-
m
ak
i
n
g
.
T
h
e
u
s
e
o
f
I
o
T
h
as
also
m
a
d
e
s
m
ar
t
f
ar
m
i
n
g
m
o
r
e
p
o
p
u
lar
as
a
m
ea
n
s
o
f
d
eliv
er
in
g
i
m
p
r
o
v
ed
an
d
ch
ea
p
er
f
o
o
d
to
th
e
g
r
o
w
i
n
g
p
o
p
u
latio
n
o
f
th
e
w
o
r
ld
.
Data
ca
n
b
e
ap
p
lie
d
,
am
o
n
g
o
th
er
th
in
g
s
,
in
s
m
ar
t
f
ar
m
i
n
g
to
tr
ac
k
th
e
p
ests
,
d
ec
r
ea
s
e
w
as
te,
an
d
u
tili
ze
th
e
a
v
ailab
le
s
p
ac
e
m
o
r
e
ef
f
icien
tl
y
[
1
5
]
-
[
1
8
]
.
T
h
e
r
ep
o
r
t
s
u
g
g
es
ts
a
s
y
s
te
m
b
ased
o
n
ar
tific
ial
i
n
telli
g
e
n
ce
(
AI
)
w
h
ic
h
u
s
es
r
ea
l
-
ti
m
e
d
ata
o
f
th
e
I
o
T
d
ev
ices,
an
d
ad
v
an
ce
d
an
al
y
tic
s
to
id
en
ti
f
y
,
p
r
ev
en
t
,
an
d
co
n
tr
o
l
p
ests
to
o
p
tim
ize
p
r
ec
is
io
n
ag
r
icu
l
tu
r
e
ac
ti
v
itie
s
[
1
9
]
-
[
2
2
]
.
T
o
en
h
an
ce
s
u
s
tain
ab
le
f
ar
m
i
n
g
,
t
h
e
ess
a
y
w
il
l
f
o
c
u
s
o
n
h
o
w
A
I
al
g
o
r
ith
m
s
an
d
r
e
m
o
te
s
en
s
i
n
g
d
ata
m
a
y
p
r
o
v
e
u
s
e
f
u
l
in
o
f
f
er
i
n
g
r
ea
l
-
ti
m
e
tr
ac
k
i
n
g
,
ea
r
l
y
d
etec
tio
n
,
an
d
co
r
r
ec
t
f
o
r
ec
asti
n
g
o
f
in
s
ec
t
p
ests
[
23
]
,
[
2
4
]
.
I
n
th
is
p
ap
er
,
w
e
ar
e
g
o
in
g
to
d
is
cu
s
s
s
tati
s
tical
an
d
d
ee
p
lear
n
in
g
m
o
d
el
s
in
d
if
f
er
en
t
a
g
r
icu
lt
u
r
al
s
ettin
g
s
w
it
h
a
p
ar
ticu
lar
f
o
cu
s
o
n
th
e
m
o
d
es
o
f
p
r
ed
ictin
g
in
s
ec
t
p
o
p
u
latio
n
s
[
2
5
]
-
[
2
7
]
.
T
h
e
p
u
r
p
o
s
e
o
f
th
e
cu
r
r
en
t
w
o
r
k
is
to
ev
alu
a
te
th
e
ex
t
e
n
t
to
w
h
ic
h
s
m
ar
t
f
ar
m
i
n
g
m
a
y
e
x
p
lo
it
A
I
tech
n
o
lo
g
y
to
s
u
p
p
o
r
t
f
ar
m
er
s
,
ag
r
ic
u
lt
u
r
al
o
r
g
an
izatio
n
s
,
a
n
d
cr
o
p
o
w
n
er
s
i
n
e
v
alu
ati
n
g
an
d
ea
r
l
y
id
en
ti
f
icatio
n
o
f
ag
r
ic
u
lt
u
r
al
d
is
ea
s
es a
n
d
p
ests
,
an
d
r
ed
u
ci
n
g
in
ter
r
u
p
tio
n
s
to
f
o
o
d
p
r
o
d
u
ctio
n
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
EW
T
h
e
i
m
p
o
r
tan
ce
o
f
id
en
t
if
y
i
n
g
p
ests
in
a
g
r
ic
u
lt
u
r
e
h
as
a
d
ir
ec
t
ef
f
ec
t
o
n
cr
o
p
o
u
tp
u
t,
f
o
o
d
s
ec
u
r
it
y
,
an
d
ec
o
n
o
m
ic
s
tab
ili
t
y
.
T
r
ad
it
io
n
al
m
et
h
o
d
s
o
f
p
est
s
u
r
v
eill
an
ce
,
s
u
c
h
as
p
h
y
s
ical
tr
ap
s
a
n
d
m
a
n
u
al
s
co
u
ti
n
g
,
ar
e
o
f
ten
ti
m
e
-
co
n
s
u
m
i
n
g
,
lab
o
r
-
in
te
n
s
i
v
e
,
a
n
d
p
r
o
n
e
to
h
u
m
an
er
r
o
r
.
T
o
en
h
a
n
ce
ac
c
u
r
ac
y
,
s
ca
lab
il
it
y
,
a
n
d
ef
f
icien
c
y
,
r
esear
ch
er
s
h
a
v
e
in
cr
ea
s
i
n
g
l
y
f
o
c
u
s
ed
o
n
au
to
m
ated
p
est
d
etec
tio
n
s
y
s
te
m
s
t
h
at
ex
p
lo
it
co
m
p
u
ter
v
is
io
n
,
m
ac
h
i
n
e
lear
n
i
n
g
,
a
n
d
d
ee
p
lear
n
in
g
.
E
ar
lier
ap
p
r
o
ac
h
es
r
elied
o
n
class
ica
l
i
m
ag
e
p
r
o
ce
s
s
i
n
g
tech
n
iq
u
es
u
s
i
n
g
co
lo
r
,
tex
tu
r
e,
an
d
s
h
ap
e
f
ea
tu
r
es,
co
m
b
in
ed
w
it
h
tr
ad
itio
n
al
clas
s
i
f
ie
r
s
s
u
ch
as
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
s
(
SV
M)
an
d
K
-
n
ea
r
e
s
t
n
e
ig
h
b
o
r
s
(
K
-
N
N)
.
A
lt
h
o
u
g
h
t
h
ese
m
et
h
o
d
s
w
er
e
m
o
d
er
atel
y
ef
f
ec
tiv
e,
t
h
eir
p
er
f
o
r
m
a
n
ce
w
a
s
s
i
g
n
if
ican
t
l
y
a
f
f
ec
ted
b
y
v
ar
iatio
n
s
in
ill
u
m
i
n
atio
n
,
n
o
i
s
e
lev
el
s
,
an
d
i
n
s
ec
t
m
o
r
p
h
o
lo
g
y
.
I
n
th
e
f
o
o
d
p
r
o
d
u
ctio
n
an
d
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o
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j
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an
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ch
an
g
e
d
etec
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ca
p
ab
ilit
ies [
1
5
]
.
I
o
T
-
b
ased
in
tellig
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n
t p
es
t d
etec
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[
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C
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tr
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l [
1
8
]
.
Desp
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s
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f
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t
p
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r
ess
,
A
I
-
d
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n
p
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d
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s
y
s
te
m
s
f
ac
e
s
ev
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al
li
m
itati
o
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s
.
Mo
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el
p
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d
ep
en
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s
o
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lar
g
e,
w
ell
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an
n
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tated
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atasets
,
w
h
ic
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r
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m
ai
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s
ca
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f
o
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r
ar
e,
im
m
at
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o
r
p
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ed
ato
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y
in
s
ec
t
s
p
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E
n
v
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o
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m
en
tal
v
ar
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s
u
c
h
as
ex
tr
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lig
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cc
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ca
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ld
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I
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ased
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s
in
tr
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ce
ch
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elate
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to
en
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g
y
co
n
s
u
m
p
tio
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,
co
m
m
u
n
icatio
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late
n
c
y
,
a
n
d
d
ep
lo
y
m
en
t
co
s
ts
.
Fu
r
t
h
er
m
o
r
e,
d
ep
lo
y
in
g
d
ee
p
lear
n
i
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g
m
o
d
el
s
o
n
lo
w
-
p
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w
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m
b
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d
d
ed
d
ev
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r
em
ai
n
s
co
m
p
u
tatio
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a
ll
y
co
n
s
tr
ain
ed
.
A
d
d
r
ess
i
n
g
t
h
ese
is
s
u
e
s
r
eq
u
ir
es
i
m
p
r
o
v
ed
d
ataset
d
i
v
er
s
it
y
,
li
g
h
t
w
ei
g
h
t
ar
c
h
itect
u
r
es,
ad
ap
tiv
e
lear
n
i
n
g
s
tr
ate
g
ie
s
,
an
d
co
s
t
-
e
f
f
ec
ti
v
e
d
ep
lo
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m
en
t
s
o
lu
t
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s
f
o
r
s
u
s
ta
in
ab
le
ag
r
ic
u
lt
u
r
al
ad
o
p
tio
n
.
3.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
p
r
esen
ts
t
h
e
co
m
p
lete
m
et
h
o
d
o
lo
g
ical
f
r
a
m
e
w
o
r
k
ad
o
p
ted
f
o
r
p
r
ed
ict
in
g
s
t
u
d
en
t
ac
ad
em
ic
.
T
h
e
in
itial
p
h
ase
o
f
th
e
p
r
o
p
o
s
ed
p
est
d
etec
tio
n
an
d
class
i
f
ica
tio
n
s
y
s
te
m
m
et
h
o
d
is
th
e
u
t
ilizatio
n
o
f
th
e
P
esto
p
ia
d
ataset
co
m
p
r
is
ed
o
f
r
elev
an
t
p
es
t
i
m
a
g
es.
T
h
er
e
is
a
s
u
p
p
le
m
en
t
o
f
th
e
d
a
ta
b
y
u
s
e
o
f
ce
n
ter
cr
o
p
p
in
g
an
d
h
o
r
izo
n
tal
f
lip
p
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g
in
o
r
d
er
to
en
h
a
n
ce
t
h
e
g
e
n
er
aliza
tio
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o
f
th
e
m
o
d
els a
n
d
au
g
m
e
n
t t
h
e
v
ar
iet
y
o
f
th
e
d
ataset.
Hig
h
-
le
v
el
r
ep
r
esen
tatio
n
s
o
f
t
h
e
p
ictu
r
es
a
r
e
th
en
r
ec
eiv
ed
af
ter
t
h
e
au
g
m
e
n
tat
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p
r
o
ce
s
s
th
r
o
u
g
h
f
ea
t
u
r
e
ex
tr
ac
tio
n
b
y
E
f
f
icien
tNet.
YOL
Ov
8
is
a
r
e
al
-
ti
m
e
o
b
j
ec
t
d
etec
to
r
th
at
is
in
s
ta
lled
in
o
r
d
er
to
d
etec
t
an
d
id
en
t
if
y
p
es
ts
i
n
th
e
i
m
a
g
es
w
it
h
ac
c
u
r
ac
y
.
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h
e
id
en
ti
f
ied
ar
ea
s
ar
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t
h
en
cla
s
s
i
f
ied
u
n
d
er
E
f
f
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tNet
-
B
7
,
in
o
r
d
er
to
d
is
tin
g
u
is
h
v
ar
io
u
s
t
y
p
e
s
o
f
p
ests
.
A
p
p
r
o
p
r
iate
m
ea
s
u
r
es
ar
e
u
s
ed
to
m
ea
s
u
r
e
p
er
f
o
r
m
a
n
ce
in
th
e
m
o
d
el
at
th
e
s
a
m
e
ti
m
e.
F
in
all
y
,
th
e
r
es
u
lts
o
f
th
e
d
etec
tio
n
ar
e
s
av
ed
in
th
e
A
d
af
r
u
it
I
O
clo
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s
er
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ice
w
h
ich
m
a
k
es t
h
e
m
ac
ce
s
s
ib
le
r
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m
o
tel
y
a
n
d
en
a
b
les r
ea
l
-
ti
m
e
m
o
n
ito
r
in
g
.
3
.
1
.
Da
t
a
s
et
i
m
a
g
e:
pes
t
o
pia
P
esto
p
ia
is
a
lar
g
e
d
atab
ase
o
f
h
ig
h
-
r
eso
lu
t
io
n
p
h
o
to
g
r
ap
h
s
o
f
5
6
,
6
8
5
co
m
m
o
n
I
n
d
ian
p
est
s
co
n
tain
i
n
g
all
th
e
in
f
o
r
m
a
tio
n
ab
o
u
t
t
h
e
i
n
s
ec
ticid
es
co
m
m
o
n
l
y
ap
p
lied
to
co
n
tr
o
l
t
h
e
m
.
T
h
e
d
ataset
i
s
s
u
p
p
o
s
ed
to
ass
i
s
t
t
h
e
r
esear
c
h
er
s
a
n
d
p
r
ac
titi
o
n
er
s
to
d
esi
g
n
a
n
d
i
m
p
r
o
v
e
m
ac
h
i
n
e
lear
n
in
g
al
g
o
r
it
h
m
s
to
id
en
ti
f
y
a
n
d
m
a
n
a
g
e
p
ests
.
A
l
s
o
,
it
w
i
ll
b
e
h
elp
f
u
l
to
f
ar
m
er
s
an
d
p
est
co
n
tr
o
l
e
x
p
er
ts
w
h
o
m
a
y
w
an
t
to
k
n
o
w
m
o
r
e
ab
o
u
t
I
n
d
ia
n
p
ests
a
n
d
m
eth
o
d
s
o
f
co
n
tr
o
llin
g
p
est
s
.
T
h
e
v
ar
iet
y
o
f
p
ests
i
n
P
esto
p
ia
p
r
esen
ts
a
n
u
n
p
ar
alle
led
o
p
p
o
r
tu
n
it
y
to
ex
p
lo
r
e
an
d
r
ev
ie
w
t
h
e
co
m
p
lex
w
o
r
ld
o
f
I
n
d
ian
p
es
t c
o
n
tr
o
l.
3
.
2
.
Da
t
a
a
ug
m
ent
a
t
io
n
Data
au
g
m
en
tatio
n
is
t
h
e
tec
h
n
iq
u
e
f
o
r
cr
ea
tin
g
n
e
w
d
ata
ar
tif
iciall
y
f
r
o
m
th
e
av
ailab
le
d
ata.
I
t
i
s
m
ai
n
l
y
u
s
ed
to
tr
ain
n
e
w
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
Fo
r
th
e
in
it
ial
tr
ain
i
n
g
o
f
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el
s
,
en
o
u
g
h
co
m
p
lex
a
n
d
lar
g
e
d
a
tasets
ar
e
r
eq
u
ir
ed
.
At
th
e
s
a
m
e
ti
m
e,
d
u
e
to
is
s
u
e
s
r
elate
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I
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R
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I
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4864
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s:
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1
5
5
1
.
B
I
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G
RAP
H
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E
S O
F
AUTH
O
RS
S
a
b
a
p
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th
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h
a
n
m
u
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m
h
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l
d
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d
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g
r
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d
a
B.
S
c
.
in
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m
p
u
ter
S
c
ien
c
e
.
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h
a
s
q
u
a
li
f
ied
b
o
th
t
h
e
Na
ti
o
n
a
l
El
ig
ib
il
it
y
T
e
st
(NE
T
)
a
n
d
th
e
S
tate
El
ig
ib
i
li
ty
T
e
st
(S
ET
).
He
is
c
u
rre
n
t
ly
p
u
rsu
i
n
g
a
P
h
.
D.
in
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o
m
p
u
ter
S
c
ien
c
e
a
t
S
R
M
In
st
it
u
te
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
(S
RM
IS
T
),
Ra
m
a
p
u
ra
m
Ca
m
p
u
s.
His
p
rim
a
r
y
re
s
e
a
rc
h
in
tere
sts
in
c
lu
d
e
d
e
e
p
lea
rn
in
g
a
n
d
th
e
in
tern
e
t
o
f
th
i
n
g
s
(Io
T
).
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h
a
s
p
u
b
l
ish
e
d
S
c
o
p
u
s
-
in
d
e
x
e
d
re
se
a
rc
h
a
rti
c
les
o
n
d
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e
p
lea
rn
in
g
-
b
a
se
d
p
e
st
d
e
tec
ti
o
n
a
n
d
c
las
sif
ica
ti
o
n
in
a
g
ricu
lt
u
re
.
His
a
c
a
d
e
m
i
c
in
tere
sts
a
lso
in
c
lu
d
e
in
tell
ig
e
n
t
sy
ste
m
s
a
n
d
a
p
p
li
e
d
m
a
c
h
in
e
lea
rn
in
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
s
s
9
7
7
1
@
srm
ist
.
e
d
u
.
in
.
Dr
.
V
ija
y
a
l
a
k
sh
m
i
Na
t
a
r
a
j
a
n
is
a
n
A
ss
istan
t
P
ro
f
e
ss
o
r
in
t
h
e
De
p
a
rtm
e
n
t
o
f
Co
m
p
u
ter
A
p
p
li
c
a
ti
o
n
s
a
t
S
RM
I
n
stit
u
te
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
.
S
h
e
re
c
e
iv
e
d
h
e
r
P
h
.
D
.
i
n
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
A
n
n
a
Un
iv
e
rsit
y
in
2
0
1
8
.
S
h
e
is
a
n
a
c
ti
v
e
m
e
m
b
e
r
o
f
v
a
rio
u
s
p
ro
f
e
ss
io
n
a
l
b
o
d
ies
,
in
c
l
u
d
i
n
g
t
h
e
Co
m
p
u
ter
S
o
c
iety
o
f
In
d
ia
(CS
I)
a
n
d
t
h
e
In
tern
a
ti
o
n
a
l
A
s
so
c
iatio
n
o
f
En
g
in
e
e
rs
(IA
ENG
).
A
d
d
it
io
n
a
ll
y
,
sh
e
se
rv
e
s
o
n
th
e
B
o
a
rd
o
f
S
tu
d
ies
a
t
P
o
n
d
ich
e
rry
Un
iv
e
rsit
y
a
s
a
n
o
m
in
e
e
.
S
h
e
h
a
s
n
u
m
e
ro
u
s
p
u
b
l
ica
ti
o
n
s
in
i
n
tern
a
ti
o
n
a
l
j
o
u
r
n
a
ls,
re
f
le
c
ti
n
g
h
e
r
d
e
d
ica
t
io
n
to
r
e
se
a
rc
h
a
n
d
a
c
a
d
e
m
ia.
Sh
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
v
ij
a
y
a
ln
@s
r
m
ist.
e
d
u
.
in
.
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