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Ag
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ize
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ro
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th
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y
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d
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m
(CYPS
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is
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t
o
p
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ld
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ro
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s o
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ir
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K
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s
:
C
r
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p
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ield
C
r
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p
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p
r
ed
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Ma
ch
in
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o
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ith
m
s
Pre
d
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Su
p
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lear
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tech
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i
q
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T
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s
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p
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c
c
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a
rticle
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d
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CC B
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li
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se
.
C
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r
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s
p
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A
uth
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r
:
Su
n
d
ay
Ad
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jag
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Dep
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tm
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t o
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m
p
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i T
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ical
Un
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s
ity
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b
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,
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Nig
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m
ail: saaja
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ch
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l.la
u
tech
.
ed
u
.
n
g
1.
I
NT
RO
D
UCT
I
O
N
Ag
r
icu
ltu
r
e
is
o
n
e
o
f
Nig
er
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’
s
m
o
s
t
im
p
o
r
ta
n
t
an
d
lar
g
est
ec
o
n
o
m
ic
ac
tiv
ities
with
a
s
ig
n
if
ican
t
im
p
ac
t
o
n
n
ati
o
n
al
d
ev
elo
p
m
e
n
t.
T
h
e
f
a
v
o
r
a
b
le
wid
e
r
an
g
e
o
f
clim
atic
v
ar
iatio
n
s
h
as
m
ad
e
th
e
co
u
n
tr
y
a
lead
er
in
th
e
p
r
o
d
u
ctio
n
o
f
v
ar
io
u
s
ty
p
es
o
f
ag
r
icu
ltu
r
al
p
r
o
d
u
cts
s
u
ch
as
m
aize
,
y
am
,
p
alm
o
il
,
p
in
ea
p
p
le,
co
co
a
,
ca
s
s
av
a,
m
illet,
an
d
s
o
o
n
.
T
h
e
o
b
jectiv
e
o
f
ag
r
icu
ltu
r
al
p
r
o
d
u
ctio
n
is
to
ac
h
iev
e
m
ax
im
u
m
cr
o
p
y
ield
[
1
]
,
[
2
]
.
T
h
e
am
o
u
n
t
o
f
a
g
r
icu
ltu
r
al
p
r
o
d
u
ctio
n
h
ar
v
ested
p
e
r
u
n
it
o
f
lan
d
ar
ea
is
r
ef
er
r
e
d
to
as
c
r
o
p
y
iel
d
[
3
]
-
[
5
]
.
So
m
etim
es,
it
is
al
s
o
r
ef
er
r
ed
to
as
“a
g
r
icu
ltu
r
al
o
u
t
p
u
t”
.
T
h
e
c
r
o
p
y
ield
wh
ich
is
th
e
p
r
im
ar
y
o
b
jectiv
e
o
f
ag
r
icu
ltu
r
al
p
r
o
d
u
ctio
n
is
af
f
ec
ted
b
y
s
o
m
e
n
o
tab
le
c
li
m
atic
co
n
d
itio
n
s
in
clu
d
in
g
r
a
in
f
all,
tem
p
er
atu
r
e,
h
u
m
id
ity
,
an
d
lan
d
f
a
cto
r
s
s
u
ch
as
s
o
il
p
H,
s
o
il
ty
p
e.
C
lim
at
e
-
d
r
iv
en
c
r
o
p
y
ield
,
y
ield
v
a
r
iab
ilit
y
an
d
clim
ate
ch
an
g
e
im
p
ac
t
s
tu
d
ied
[
6
]
s
u
g
g
ested
th
at
wea
th
er
an
d
clim
atic
f
ac
to
r
s
ar
e
th
e
p
r
o
m
in
e
n
t
d
r
iv
er
s
o
f
c
r
o
p
y
ield
.
Hen
ce
,
ce
r
tain
f
ea
tu
r
es
lik
e
r
a
in
f
all
an
d
tem
p
er
at
u
r
e
t
h
at
h
a
v
e
s
ig
n
if
ican
t
ef
f
ec
ts
o
n
cr
o
p
y
ield
s
ar
e
af
f
ec
ted
o
n
ce
clim
atic
ch
an
g
es ten
d
to
war
d
s
th
e
u
n
f
a
v
o
r
a
b
le
s
id
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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n
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J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
r
ed
ictive
a
n
a
lytics o
n
cro
p
yi
eld
u
s
in
g
s
u
p
ervis
ed
lea
r
n
in
g
tech
n
iq
u
es
(
Ju
liu
s
Ola
tu
n
ji Ok
eso
la
)
1665
T
h
e
s
tu
d
y
an
d
m
an
a
g
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en
t
o
f
co
m
p
licated
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ce
n
ar
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lik
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cr
o
p
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ield
p
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ed
ictio
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ca
n
th
er
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o
r
e
h
elp
to
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en
d
er
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lar
g
e
r
r
etu
r
n
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d
p
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o
m
o
te
p
r
o
f
itab
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y
.
Sin
ce
m
ac
h
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lear
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in
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is
r
e
g
ar
d
e
d
as
an
ef
f
ec
tiv
e
to
o
l
[7
]
,
[
8]
f
o
r
cr
o
p
y
ield
p
r
ed
ictio
n
[
9
]
as
it
is
u
s
ed
to
d
eter
m
in
e
p
atter
n
s
,
co
r
r
elatio
n
s
an
d
k
n
o
wled
g
e
f
r
o
m
d
atasets
[
1
0
]
.
T
h
is
r
esear
ch
ex
te
n
d
s
th
e
s
tu
d
y
o
f
[
1
1
]
o
n
a
r
tific
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in
tellig
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(
AI
)
aim
in
g
at
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illi
n
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ap
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eseen
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ld
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s
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ield
.
T
h
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c
r
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p
s
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th
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aize
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o
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ith
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s
ig
h
t
in
to
th
e
f
u
tu
r
e
an
d
k
n
o
w
wh
et
h
er
th
e
cr
o
p
s
h
e
wan
ts
to
p
lan
t
will
y
ield
wel
l
o
r
n
o
t.
T
h
e
r
esear
ch
co
n
tr
ib
u
tio
n
s
:
i)
e
v
alu
ate
t
h
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
e
n
t
alg
o
r
ith
m
s
to
d
eter
m
in
e
th
e
b
est
;
an
d
ii)
d
ev
elo
p
a
p
r
ed
ictio
n
s
y
s
tem
u
s
in
g
th
e
b
est
-
d
eter
m
in
ed
alg
o
r
ith
m
.
T
h
e
r
em
ain
d
er
o
f
th
e
ar
ticle
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
ti
o
n
2
ex
am
in
es
th
e
ea
r
lier
r
esear
ch
an
d
an
aly
s
is
.
T
h
e
s
u
g
g
ested
s
ch
em
e
’
s
m
eth
o
d
o
lo
g
y
is
s
h
o
wn
i
n
s
ec
tio
n
3
f
o
r
th
e
c
r
o
p
y
ield
p
r
ed
ictio
n
s
y
s
tem
(
C
YPS).
I
n
s
ec
tio
n
4
p
r
esen
t
s
th
e
im
p
lem
en
tatio
n
,
co
n
clu
s
io
n
s
,
an
d
d
is
cu
s
s
io
n
.
I
n
s
ec
t
io
n
5
o
f
th
e
s
tu
d
y
p
r
esen
ts
its
f
in
d
in
g
s
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
K
au
r
[
1
2
]
ev
al
u
ated
th
e
ap
p
l
icatio
n
s
ar
ea
s
in
th
e
f
ield
o
f
ag
r
icu
ltu
r
e
i
n
clu
d
in
g
f
o
r
ec
asti
n
g
,
s
m
ar
t
ir
r
ig
atio
n
s
y
s
tem
,
cr
o
p
s
elec
tio
n
,
s
to
r
ag
e
s
y
s
tem
s
,
an
d
c
r
o
p
d
i
s
ea
s
e
p
r
ed
ictio
n
to
ag
r
ee
with
[
1
3
]
th
at,
c
r
o
p
y
ield
p
r
ed
ictio
n
is
a
cr
itical
r
esp
o
n
s
i
b
ilit
y
o
f
d
ec
is
io
n
-
m
ak
e
r
s
,
ex
p
e
r
ts
,
an
d
f
a
r
m
er
s
at
th
e
n
atio
n
al
an
d
r
e
g
io
n
al
le
v
els.
Hen
ce
,
m
aize
was
as
a
co
n
tr
o
l
to
f
ig
u
r
e
o
u
t
th
e
im
p
ac
t
o
f
wea
th
er
o
n
ag
r
icu
ltu
r
al
p
r
o
d
u
ce
an
d
ad
m
itted
th
at
ag
r
icu
ltu
r
al
y
ield
s
ar
e
tr
u
ly
s
u
s
ce
p
tib
le
to
ex
tr
em
e
wea
th
er
an
d
th
at
ch
an
g
es in
th
e
m
ea
n
a
n
d
ex
tr
em
e
wea
th
er
p
o
s
e
a
s
ig
n
if
ican
t d
a
n
g
er
t
o
g
o
v
er
n
m
en
ts
an
d
o
r
g
an
izatio
n
s
[
1
4
]
,
[
1
5
]
.
W
in
ter
W
h
ea
t
f
o
r
in
s
tan
ce
,
is
p
ar
ticu
lar
ly
v
u
ln
e
r
ab
le
to
l
o
w
t
em
p
er
atu
r
es
(
f
r
ee
zin
g
)
in
t
h
e
f
all,
as
well
as
to
h
ea
t
s
tr
e
s
s
d
u
r
in
g
g
r
ain
f
illi
n
g
an
d
s
tem
elo
n
g
atio
n
[
1
6
]
.
T
h
is
v
u
ln
er
a
b
ilit
y
to
s
ev
er
e
tem
p
er
atu
r
es
is
th
er
ef
o
r
e
ass
u
m
ed
to
b
e
t
h
e
d
ec
lin
in
g
ca
u
s
e
o
f
wh
ea
t
y
ield
s
th
r
o
u
g
h
o
u
t
E
u
r
o
p
e
[
3
]
.
Me
a
n
wh
ile,
th
e
f
ac
to
r
s
in
f
lu
en
cin
g
Fall
Ar
m
y
w
o
r
m
d
am
ag
e
o
n
th
e
A
f
r
ican
m
aize
f
i
eld
an
d
its
q
u
an
tify
i
n
g
im
p
ac
t
s
h
av
e
b
ee
n
wid
el
y
s
tu
d
ied
b
y
m
a
n
y
a
u
th
o
r
s
in
clu
d
in
g
[
3
]
,
[
1
6
]
to
c
o
n
clu
d
e
th
at
th
e
Fall Ar
m
y
wo
r
m
ca
u
s
es
s
u
b
s
tan
tial d
am
ag
e
to
m
aize
an
d
s
o
m
e
o
th
e
r
cr
o
p
s
,
an
d
th
at
th
e
p
o
ten
tial
d
am
ag
e
s
m
ay
b
e
g
r
ea
tly
m
i
n
im
ized
b
y
r
eg
u
lar
wee
d
in
g
.
T
o
war
d
s
im
p
r
o
v
i
n
g
p
r
o
d
u
ctiv
ity
th
r
o
u
g
h
cr
o
p
y
ield
p
r
ed
ictio
n
th
er
ef
o
r
e,
m
an
y
r
esear
c
h
er
s
in
clu
d
in
g
[
1
7
]
ar
e
r
ec
en
tly
f
o
c
u
s
in
g
o
n
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
an
d
th
eir
u
s
es.
Vee
n
ad
h
ar
i
et
a
l
.
[
9
]
an
d
Mo
r
ay
e
et
a
l.
[
1
8
]
s
p
ec
if
ica
lly
em
p
lo
y
ed
m
o
s
t
in
f
lu
e
n
ci
n
g
clim
atic
p
ar
am
eter
s
o
n
cr
o
p
y
ield
to
t
r
ain
C
4
.
5
alg
o
r
ith
m
an
d
d
e
m
o
n
s
tr
ated
th
e
u
s
e
o
f
d
ata
m
in
in
g
tech
n
iq
u
es
in
p
r
ed
ictin
g
cr
o
p
y
ield
s
.
T
h
eir
r
esu
lts
s
h
o
wed
th
at
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
es
h
av
e
b
etter
s
k
ills
in
cr
o
p
y
ield
p
r
ed
ictin
g
c
o
m
p
a
r
ed
to
th
e
p
r
in
cip
al
r
eg
r
ess
io
n
.
Similar
ly
,
Ad
eb
iy
i
et
a
l.
[
1
9
]
d
is
cu
s
s
ed
th
e
o
p
tim
izatio
n
o
f
f
ar
m
lan
d
an
d
m
o
n
ito
r
in
g
o
f
c
r
o
p
s
b
y
d
e
v
elo
p
in
g
a
p
r
ed
icti
o
n
s
y
s
tem
u
s
in
g
a
m
ac
h
in
e
le
ar
n
in
g
alg
o
r
ith
m
to
an
aly
ze
an
d
class
if
y
d
ataset
co
n
tain
in
g
s
o
m
e
p
ar
am
ete
r
s
r
elate
d
to
th
e
y
ield
o
f
c
r
o
p
s
.
T
h
is
m
o
b
ile
ap
p
licatio
n
g
u
ar
an
tees
f
ar
m
e
r
s
in
s
tan
t
in
f
o
r
m
atio
n
an
d
s
er
v
ices
n
ee
d
ed
in
th
eir
f
ar
m
lan
d
.
J
eo
n
g
et
a
l.
[
2
0
]
also
in
v
esti
g
ated
th
e
in
f
lu
en
ce
o
f
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
R
F
alg
o
r
ith
m
o
n
th
e
p
r
ed
ictin
g
o
f
th
e
y
ield
o
f
wh
ea
t,
m
aize
,
an
d
p
o
tato
cr
o
p
to
s
u
b
m
it
th
at
th
e
R
F
alg
o
r
ith
m
is
a
n
efficien
t
to
o
l
to
p
r
ed
ict
cr
o
p
y
ield
.
T
h
is
ag
r
ee
s
with
th
e
f
i
n
d
in
g
s
o
f
San
g
ee
t
a
an
d
Sh
r
u
th
i
[
2
1
]
wh
er
e
th
e
p
er
f
o
r
m
an
ce
o
f
R
F,
p
o
ly
n
o
m
ial
r
eg
r
ess
io
n
(
PR
)
,
an
d
d
ec
is
io
n
tr
ee
(
DT
)
wer
e
ev
alu
ated
an
d
R
F
was
ad
ju
d
g
ed
th
e
b
est
alg
o
r
ith
m
f
o
r
c
r
o
p
p
r
ed
ictio
n
b
ased
o
n
th
e
ac
c
u
r
ac
y
o
f
t
h
e
test
in
g
an
d
tr
ain
in
g
r
esu
lts
,
less
er
p
r
o
ce
s
s
in
g
tim
e,
an
d
b
etter
p
er
f
o
r
m
an
ce
ev
en
wh
en
h
an
d
lin
g
lar
g
e
am
o
u
n
t o
f
d
ata.
Me
an
wh
ile,
s
o
m
e
au
th
o
r
s
h
a
v
e
b
ee
n
em
p
lo
y
in
g
p
e
r
f
o
r
m
an
c
e
m
etr
ics
to
ev
alu
ate
t
h
e
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
in
o
t
h
er
to
p
u
t
s
o
m
e
s
p
ec
u
latio
n
s
ab
o
u
t
th
e
ac
cu
r
ac
y
o
f
th
e
test
r
esu
lts
to
r
est.
Fo
r
in
s
tan
ce
,
Sh
ah
et
a
l.
[
2
2
]
ev
alu
ated
m
u
ltiv
ar
iate
p
o
ly
n
o
m
ial
r
eg
r
ess
io
n
(
MPR
)
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
r
eg
r
ess
io
n
(
SVM)
,
an
d
R
F
b
ase
o
n
f
o
u
r
p
er
f
o
r
m
a
n
ce
m
etr
ic
s
-
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
M
SE)
,
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
,
m
ed
ian
a
b
s
o
lu
te
er
r
o
r
(
Md
AE
)
,
an
d
R
-
s
q
u
ar
ed
v
al
u
es.
Go
n
za
lez
-
S
an
c
h
ez
et
a
l.
[
2
3
]
also
co
m
p
ar
ed
th
e
p
r
ed
ictin
g
p
o
w
er
o
f
v
a
r
io
u
s
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
m
a
k
in
g
u
s
e
o
f
p
er
f
o
r
m
a
n
ce
m
etr
ics
R
MSE
,
R
R
SE,
an
d
MA
E
to
c
o
n
clu
d
e
th
at
M5
-
Prim
e
was
th
e
b
est
with
th
e
lar
g
est
n
u
m
b
er
o
f
cr
o
p
y
ield
s
an
d
lo
west
er
r
o
r
r
ate.
So
m
e
o
f
th
e
s
e
co
m
m
o
n
ly
u
s
ed
p
er
f
o
r
m
an
ce
m
etr
ics
an
d
th
ei
r
m
ath
em
at
ical
ex
p
r
ess
io
n
s
ar
e
as d
ep
icted
in
T
ab
le
1
.
3.
M
E
T
H
O
D
Usi
n
g
lab
eled
d
ata
s
ets
to
tr
ain
alg
o
r
ith
m
s
th
at
r
elia
b
ly
id
en
tif
y
d
ata
o
r
p
r
e
d
ict
o
u
tco
m
es.
T
h
e
s
u
p
er
v
is
ed
lear
n
i
n
g
ap
p
r
o
ac
h
is
u
s
ed
in
th
is
wo
r
k
.
T
h
i
s
is
a
lear
n
in
g
p
r
o
ce
s
s
th
at
co
n
v
er
ts
k
n
o
wn
i
n
p
u
t
in
to
o
u
tp
u
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
6
64
-
1
6
73
1666
T
ab
le
1
.
Per
f
o
r
m
an
ce
m
etr
ics an
d
th
eir
m
at
h
em
atica
l e
x
p
r
es
s
io
n
s
s/
n
P
e
r
f
o
r
ma
n
c
e
m
e
t
r
i
c
s
Ex
p
r
e
ssi
o
n
1
R
o
o
t
-
r
e
l
a
t
i
v
e
sq
u
a
r
e
e
r
r
o
r
(
R
R
S
E)
√
∑
(
+
̂
)
2
−
1
∑
(
−
̅
)
2
−
1
.
100
2
R
M
S
E
√
∑
(
+
̂
̅
)
2
−
1
3
M
A
E
(
∑
|
−
̂
|
−
1
(
)
(
̅
)
)
.
100
4
M
d
A
E
M
e
d
i
a
n
(
|
X
i
-
̃
|)
5
R
-
sq
u
a
r
e
d
v
a
l
u
e
s
∑
(
−
̅
)
(
̂
−
̂
̅
)
−
1
√
∑
(
−
̅
)
2
−
1
√
∑
(
̂
−
̂
̅
)
2
−
1
3
.
1
.
Da
t
a
s
et
T
h
e
en
tire
d
ataset
u
s
ed
t
o
im
p
lem
en
t
th
is
p
r
e
d
ictio
n
s
y
s
tem
is
liv
e
o
n
d
ata
wo
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ata.
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[
2
4
]
.
R
eg
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n
am
e
,
cr
o
p
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r
,
ar
ea
(
in
h
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ctar
es),
y
ield
(
in
to
n
s
)
,
r
ain
f
all
,
r
elativ
e
h
u
m
id
ity
,
an
d
s
o
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r
ad
iatio
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s
h
o
wn
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n
Fig
u
r
e
1
.
His
to
r
ical
d
ata
o
f
th
ese
p
ar
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eter
s
a
r
e
s
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e
d
in
a
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ile,
a
n
d
d
iv
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e
d
in
to
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p
ar
ts
a
p
ar
t
(
8
0
%)
o
f
th
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ataset
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u
s
ed
f
o
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tr
ain
in
g
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e
m
o
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an
d
th
e
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et
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test
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g
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o
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el.
Fig
u
r
e
1
.
Data
s
et
3
.
2
.
M
a
chine
lea
rning
a
lg
o
ri
t
hm
s
T
h
e
p
r
o
b
lem
to
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e
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ed
in
t
h
is
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d
y
r
eq
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ir
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d
a
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eg
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n
tech
n
i
q
u
e
wh
ich
is
a
m
o
d
eli
n
g
task
th
at
in
v
o
lv
es
p
r
ed
ictin
g
a
n
u
m
e
r
ic
v
alu
e
b
y
g
i
v
en
in
p
u
t.
T
h
e
f
o
l
lo
win
g
alg
o
r
ith
m
s
a
r
e
th
e
r
ef
o
r
e
co
n
s
id
er
ed
b
ased
o
n
th
eir
in
d
iv
id
u
al
q
u
alities
as sp
ec
if
ied
.
−
RF
:
is
th
e
m
o
s
t
p
o
p
u
lar
an
d
p
o
wer
f
u
l
s
u
p
e
r
v
is
ed
m
ac
h
i
n
e
l
ea
r
n
in
g
al
g
o
r
ith
m
an
d
it
is
ca
p
ab
le
o
f
s
o
lv
in
g
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
p
r
o
b
lem
s
[
2
5
]
.
Ov
e
r
f
itti
n
g
(
wh
ich
o
cc
u
r
s
wh
en
th
er
e
ar
e
s
o
m
an
y
f
alse
p
o
s
itiv
es)
o
f
th
e
tr
ain
i
n
g
s
et
is
n
o
t a
n
is
s
u
e
[
2
6
]
.
−
Ad
aBo
o
s
t
r
eg
r
ess
o
r
:
t
h
is
is
a
b
o
o
s
tin
g
a
p
p
r
o
ac
h
u
s
ed
in
m
ac
h
in
e
lear
n
in
g
as
an
en
s
em
b
le
m
eth
o
d
a
n
d
h
elp
s
to
ca
p
tu
r
e
v
ar
i
o
u
s
n
o
n
-
lin
ea
r
co
r
r
elatio
n
s
r
esu
ltin
g
in
im
p
r
o
v
ed
p
r
e
d
ictio
n
ac
c
u
r
ac
y
o
n
th
e
p
r
o
b
lem
o
f
in
ter
est [
2
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
r
ed
ictive
a
n
a
lytics o
n
cro
p
yi
eld
u
s
in
g
s
u
p
ervis
ed
lea
r
n
in
g
tech
n
iq
u
es
(
Ju
liu
s
Ola
tu
n
ji Ok
eso
la
)
1667
−
E
x
tr
a
tr
ee
r
e
g
r
ess
o
r
:
th
is
is
a
tr
ee
s
r
eg
r
ess
io
n
s
y
s
tem
with
ex
tr
em
ely
r
a
n
d
o
m
ize
d
tr
ee
s
[2
7
]
th
at
in
v
o
lv
es
h
ea
v
ily
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an
d
o
m
izin
g
b
o
t
h
attr
i
b
u
te
an
d
cu
t
-
p
o
in
t selec
tio
n
s
.
−
SGD:
SDG
is
a
q
u
ick
an
d
ea
s
y
way
to
f
it
lin
ea
r
class
if
ier
s
an
d
r
eg
r
ess
o
r
s
(
SVM
,
LR
)
to
co
n
v
ex
lo
s
s
f
u
n
ctio
n
s
.
−
L
in
ea
r
r
e
g
r
ess
io
n
:
L
R
is
a
m
o
d
el
f
o
r
d
ete
r
m
in
in
g
th
e
c
o
n
n
ec
tio
n
b
etwe
en
in
p
u
t
an
d
o
u
tp
u
t
n
u
m
er
ical
v
ar
iab
les th
at
was e
s
tab
lis
h
ed
in
th
e
f
ield
o
f
s
tatis
tics
[
2
7
]
.
3
.
3
.
Sy
s
t
e
m
a
rc
hite
ct
ure
T
h
e
s
y
s
tem
ar
ch
itectu
r
e
is
r
ep
r
esen
ted
in
Fig
u
r
e
2
an
d
d
e
f
in
es
th
e
co
n
ce
p
tu
al
m
o
d
el
o
f
th
e
s
y
s
tem
in
m
u
ltip
le
v
iews
an
d
s
tr
u
ctu
r
es.
Fig
u
r
e
2
ab
o
v
e
s
h
o
ws
th
e
ar
ch
itectu
r
al
d
esig
n
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
tem
f
o
r
th
e
p
r
o
ject
.
T
h
e
a
b
o
v
e
ar
ch
itectu
r
e
clea
r
ly
e
x
p
lain
s
th
e
p
r
o
ce
s
s
es
in
v
o
lv
e
in
ac
h
iev
in
g
th
e
cr
o
p
y
ield
an
d
h
o
w
all
th
e
co
m
p
o
n
en
ts
o
f
th
e
s
y
s
tem
co
m
m
u
n
icate
with
o
n
e
an
o
th
e
r
,
s
tar
tin
g
f
r
o
m
d
ata
i
n
p
u
t t
o
r
esu
lt.
Fo
llo
win
g
th
e
p
r
o
ce
s
s
es
co
n
tain
ed
b
y
t
h
e
ar
ch
itectu
r
e,
th
e
cr
o
p
y
ield
is
b
ein
g
p
r
e
d
icted
b
y
th
e
p
r
o
p
o
s
ed
s
y
s
tem
.
T
h
is
ar
ch
itectu
r
e
d
is
p
lay
ed
u
s
er
co
n
n
ec
tio
n
t
o
th
e
s
y
s
tem
an
d
s
h
o
ws
clea
r
ly
h
o
w
d
ata
is
ca
p
tu
r
ed
,
th
e
d
ata
is
th
en
p
r
ep
r
o
ce
s
s
ed
to
r
em
o
v
e
ev
er
y
u
n
wa
n
ted
d
ata
s
u
ch
as
NUL
L
,
an
d
u
n
wan
te
d
f
ea
tu
r
es.
Af
ter
p
r
ep
r
o
ce
s
s
in
g
,
th
e
d
ataset
is
th
en
d
iv
id
ed
in
to
two
.
A
p
ar
t
(
8
0
%)
o
f
th
e
d
ataset
as
th
e
tr
ain
in
g
s
et
an
d
th
e
o
th
er
p
a
r
t
(
2
0
%)
as
th
e
test
in
g
s
et.
T
h
e
tr
ain
in
g
s
et
is
u
s
ed
to
tr
ain
th
e
m
o
d
el
an
d
th
e
test
in
g
s
et
to
test
th
e
m
o
d
e
l.
Fig
u
r
e
2
.
Sy
s
tem
ar
c
h
itectu
r
e
T
h
en
d
if
f
er
e
n
t
m
ac
h
in
e
lear
n
i
n
g
al
g
o
r
ith
m
s
ar
e
a
p
p
lied
to
b
u
ild
m
o
d
els,
t
h
e
m
o
d
els
’
p
er
f
o
r
m
an
ce
s
ar
e
th
en
e
v
alu
ated
u
s
in
g
d
if
f
er
en
t
p
er
f
o
r
m
a
n
ce
m
etr
ics.
T
h
e
b
est
p
er
f
o
r
m
e
d
m
o
d
el
is
th
en
p
ass
ed
to
t
h
e
im
p
lem
en
tatio
n
s
tag
e.
T
h
e
im
p
lem
en
tatio
n
p
h
ase
co
n
tain
s
t
h
e
m
o
d
el
o
f
th
e
b
est p
er
f
o
r
m
e
d
m
o
d
el
an
d
it tak
es
in
p
u
ts
f
r
o
m
th
e
f
r
o
n
ten
d
wh
ic
h
a
r
e
p
r
o
ce
s
s
ed
with
in
th
e
p
h
a
s
e.
Yield
o
u
tp
u
t
is
b
ein
g
s
en
t
b
ac
k
to
t
h
e
f
r
o
n
ten
d
.
T
h
e
f
r
o
n
ten
d
p
h
ase
is
co
n
n
ec
t
ed
to
a
d
ata
b
ase,
wh
ich
allo
w
f
o
r
u
s
er
r
eg
is
tr
atio
n
an
d
au
th
e
n
ti
ca
tio
n
.
4.
I
M
P
L
E
M
E
NT
A
T
I
O
N
,
R
E
E
SUL
T
,
AND
DI
SCUS
SI
O
N
T
h
e
p
r
e
d
ictio
n
s
y
s
tem
was
im
p
lem
en
ted
u
s
in
g
p
y
th
o
n
p
r
o
g
r
am
m
in
g
lan
g
u
a
g
e
.
J
u
p
y
ter
p
la
tf
o
r
m
was
u
s
ed
f
o
r
s
im
u
latio
n
.
T
h
e
d
atas
et
was
p
r
ep
r
o
ce
s
s
ed
to
r
em
o
v
e
all
u
n
wan
ted
p
ar
a
m
eter
s
,
n
u
ll
v
ar
iab
les,
an
d
also
to
co
n
v
er
t
s
tr
in
g
v
a
r
iab
les
to
n
u
m
b
er
s
.
T
h
e
g
r
ap
h
s
b
elo
w
ar
e
to
s
h
o
w
th
e
d
is
tr
ib
u
tio
n
s
,
co
r
r
elatio
n
s
,
an
d
r
elatio
n
s
h
ip
s
b
etwe
en
s
o
m
e
o
f
th
e
im
p
o
r
ta
n
t p
ar
am
ete
r
s
th
at
m
o
s
tly
co
n
tr
ib
u
ted
to
th
e
p
r
ed
ictio
n
.
Fig
u
r
e
3
d
e
p
ict
th
e
L
in
ep
l
o
t
o
f
r
ain
f
all
ag
ain
s
t
p
r
o
d
u
ctio
n
,
it
is
a
lin
e
p
lo
t
o
f
r
ai
n
f
all
ag
ain
s
t
p
r
o
d
u
ctio
n
.
T
h
e
ch
a
r
t
s
h
o
ws
th
at
an
in
cr
e
ase
in
r
ain
f
all
lead
s
to
an
in
cr
ea
s
e
in
p
r
o
d
u
ctio
n
.
T
h
is
m
ea
n
s
th
at
r
ain
f
all
is
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
6
64
-
1
6
73
1668
cr
u
cial
p
ar
am
eter
in
cr
o
p
y
ield
.
Fig
u
r
e
4
,
r
e
p
r
esen
ts
th
e
b
a
r
ch
ar
t
o
f
t
em
p
e
r
atu
r
e
a
g
ain
s
t
r
eg
io
n
.
T
h
e
c
h
ar
t
s
h
o
ws
th
at
Kad
u
n
a
h
as
th
e
h
ig
h
est
av
er
ag
e
tem
p
e
r
atu
r
e,
f
o
llo
wed
b
y
Kan
o
,
Po
r
th
ar
co
u
r
t,
an
d
I
b
a
d
a
n
r
esp
ec
tiv
ely
.
Fig
u
r
e
3
.
L
i
n
e
p
lo
t
o
f
r
ain
f
all
ag
ain
s
t p
r
o
d
u
ctio
n
Fig
u
r
e
4
.
B
ar
ch
a
r
t o
f
r
eg
io
n
a
g
ain
s
t te
m
p
er
atu
r
e
4
.
1
.
T
ra
ini
ng
s
et
a
nd
t
esting
s
et
T
h
e
d
ata
s
et
was
d
iv
id
ed
in
to
two
8
0
%
o
f
th
e
d
ata
s
et
f
o
r
t
r
ain
in
g
d
ata
a
n
d
th
e
r
em
ain
in
g
2
0
%
f
o
r
test
in
g
.
T
h
e
alg
o
r
ith
m
f
o
r
s
p
litt
in
g
th
e
d
ataset
is
d
ep
icted
in
Fig
u
r
e
5
.
Fo
r
th
e
x
-
ax
is
,
th
e
tr
ain
in
g
s
et
h
as
a
to
tal
o
f
8
3
r
o
ws
an
d
8
co
lu
m
n
s
wh
ile
th
e
test
in
g
s
et
in
to
tal
h
a
s
2
1
r
o
ws
an
d
8
co
lu
m
n
s
.
Mo
d
el
d
ev
elo
p
m
en
t
is
s
h
o
wn
in
Fig
u
r
e
6
.
Fig
u
r
e
5
.
Sp
litt
in
g
d
ataset
Fig
u
r
e
6
.
Mo
d
els’
d
ev
elo
p
m
e
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
r
ed
ictive
a
n
a
lytics o
n
cro
p
yi
eld
u
s
in
g
s
u
p
ervis
ed
lea
r
n
in
g
tech
n
iq
u
es
(
Ju
liu
s
Ola
tu
n
ji Ok
eso
la
)
1669
4
.
2
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n o
n r
eg
re
s
s
io
n a
lg
o
rit
hm
s
T
h
is
p
r
o
b
lem
r
eq
u
ir
es
r
eg
r
ess
io
n
tech
n
iq
u
e,
wh
ich
is
an
u
n
s
u
p
er
v
is
ed
lear
n
i
n
g
m
eth
o
d
.
Var
io
u
s
r
eg
r
ess
io
n
alg
o
r
ith
m
s
wer
e
u
s
ed
to
b
u
ild
m
o
d
els.
So
m
e
o
f
t
h
ese
alg
o
r
ith
m
s
wer
e
u
s
ed
i
n
ex
is
tin
g
liter
atu
r
e.
Fiv
e
r
eg
r
ess
io
n
alg
o
r
ith
m
s
wer
e
u
s
ed
f
o
r
th
e
s
tu
d
y
.
T
h
ese
alg
o
r
ith
m
s
ar
e
R
F,
S
GD,
E
x
tr
aT
r
ee
r
eg
r
ess
o
r
(
E
T
R
)
,
Ad
aBo
o
s
t
r
eg
r
ess
o
r
,
an
d
lin
ea
r
r
eg
r
ess
io
n
.
4
.
3
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n us
ing
perf
o
rm
a
nce
m
et
rics
Per
f
o
r
m
an
ce
m
etr
ics
u
s
ed
to
ev
alu
ate
m
o
d
els
d
ev
elo
p
ed
w
ith
r
eg
r
ess
io
n
al
g
o
r
ith
m
s
a
r
e
R
2
_
s
co
r
e,
MA
E
,
MSE
,
Md
AE
,
R
MSE
,
an
d
m
ea
n
a
b
s
o
lu
te
p
er
ce
n
tag
e
e
r
r
o
r
(
MA
PE)
.
Fig
u
r
e
7
s
h
o
ws
p
er
f
o
r
m
an
ce
o
f
th
e
v
ar
io
u
s
m
o
d
els
em
p
lo
y
ed
i
n
t
h
is
s
tu
d
y
b
y
m
ea
n
s
o
f
R
2
_
s
co
r
e.
T
h
e
h
ig
h
est
attain
ab
le
R
2
_
s
co
r
e
v
alu
e
is
1
.
0
,
th
e
clo
s
er
th
e
v
alu
e
is
to
1
t
h
e
b
etter
th
e
m
o
d
el.
Fig
u
r
e
8
d
e
p
ict
er
r
o
r
m
etr
ics
wh
ich
ar
e
MA
E
,
MSE
,
Md
AE
,
R
MSE
,
an
d
MA
PE
o
f
th
e
f
iv
e
m
o
d
els.
T
h
e
lo
wer
th
e
v
alu
e
o
f
er
r
o
r
s
th
e
b
etter
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
alg
o
r
i
th
m
.
4
.
4
.
Dis
cus
s
io
n o
f
f
ind
ing
s
T
h
e
r
esu
lt
f
r
o
m
t
h
e
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
o
f
th
e
s
elec
ted
alg
o
r
ith
m
s
s
h
o
ws
th
at
SGD
h
as
p
er
f
o
r
m
ed
a
lo
t
b
etter
co
m
p
ar
ed
to
o
th
e
r
s
.
I
t
h
as
a
h
ig
h
er
R
2
_
s
co
r
e
an
d
h
as
lo
wer
v
alu
es
o
f
er
r
o
r
s
co
m
p
ar
ed
to
o
t
h
er
alg
o
r
ith
m
s
.
T
h
e
r
ef
o
r
e
,
SGD
was selec
ted
am
o
n
g
o
th
e
r
s
to
i
m
p
lem
en
t th
e
p
r
ed
ictio
n
s
y
s
tem
.
Fig
u
r
e
7
d
ep
ict
R
2
_
s
co
r
e
p
er
f
o
r
m
an
ce
m
etr
ics
an
d
also
s
h
o
w
s
th
at
th
e
E
x
tr
aT
r
ee
r
eg
r
ess
o
r
h
as
a
b
etter
R
2
_
s
co
r
e
co
m
p
ar
ed
to
lin
ea
r
r
e
g
r
ess
io
n
.
B
u
t
th
e
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
o
f
b
o
th
alg
o
r
ith
m
s
u
s
in
g
er
r
o
r
m
etr
ics
as
r
ep
r
esen
ted
in
Fig
u
r
e
8
s
h
o
ws
th
at
l
in
ea
r
r
eg
r
ess
io
n
h
a
s
lo
wer
MA
E
,
M
d
AE
,
a
n
d
MA
PE
co
m
p
ar
ed
to
E
x
tr
aT
r
ee
r
eg
r
ess
o
r
.
W
h
ile
E
x
tr
aT
r
ee
r
eg
r
ess
o
r
h
as
lo
wer
MSE
an
d
R
MSE
co
m
p
ar
e
d
t
o
l
in
ea
r
r
e
g
r
ess
io
n
.
T
h
er
ef
o
r
e,
if
an
al
g
o
r
ith
m
h
a
s
a
h
ig
h
er
R
2
_
s
co
r
e
d
o
es
n
o
t
m
ea
n
th
e
alg
o
r
ith
m
is
b
etter
co
m
p
ar
ed
to
o
t
h
er
s
.
W
h
en
s
elec
tin
g
th
e
al
g
o
r
ith
m
to
ad
ap
t
f
o
r
a
p
r
o
ject,
th
e
alg
o
r
ith
m
s
h
o
u
l
d
n
o
t
b
e
ev
alu
ate
d
b
ased
o
n
R
2
_
s
co
r
e
p
er
f
o
r
m
an
ce
alo
n
e,
d
if
f
er
en
t e
r
r
o
r
m
et
r
ics s
h
o
u
ld
also
b
e
p
u
t
in
to
co
n
s
id
er
atio
n
.
Fig
u
r
e
7
.
R
2
_
s
co
r
e
p
er
f
o
r
m
an
ce
m
etr
ic
Fig
u
r
e
8
.
E
r
r
o
r
m
etr
ics
4
.
5
.
Cro
p
y
ield pre
dict
io
n s
y
s
t
em
T
h
e
p
r
ed
ictio
n
s
y
s
tem
was
im
p
lem
en
ted
u
s
in
g
p
y
t
h
o
n
p
r
o
g
r
a
m
m
in
g
lan
g
u
ag
e
as
d
ep
icted
in
Fig
u
r
e
9
.
T
h
e
p
r
e
d
ictio
n
s
y
s
tem
is
th
en
co
n
n
ec
ted
to
a
we
b
i
n
ter
f
ac
e
f
o
r
ef
f
icien
t
u
s
ag
e.
T
h
e
alg
o
r
ith
m
u
s
ed
to
b
u
ild
th
e
m
o
d
el
f
o
r
th
e
C
YPS
is
SGD.
T
h
e
p
r
ed
ictio
n
s
y
s
tem
is
a
p
y
th
o
n
c
o
d
e
(
p
r
ed
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r
.
p
y
)
th
at
allo
ws
th
e
en
ter
in
g
o
f
n
ew
in
p
u
ts
.
T
h
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in
p
u
ts
s
u
p
p
lied
ar
e
co
llected
b
y
th
e
m
o
d
el
b
u
ilt
w
h
ich
is
ca
p
ab
le
o
f
p
r
o
ce
s
s
in
g
ad
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itio
n
al
d
ata
to
m
ak
e
p
r
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tio
n
s
.
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h
e
p
r
e
d
ictio
n
s
y
s
tem
p
r
o
ce
s
s
es
th
e
s
u
p
p
lied
d
ata,
p
r
ed
ict
s
th
e
v
alu
e
o
f
y
(
c
r
o
p
y
ield
)
,
an
d
th
e
n
d
is
p
lay
s
th
e
p
r
ed
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r
esu
lt
o
f
ex
p
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ted
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r
o
d
u
ctio
n
.
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r
ea
s
y
u
s
ag
e
o
f
th
e
p
r
ed
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n
s
y
s
te
m
b
y
th
e
u
s
er
s
,
th
e
p
y
th
o
n
co
d
e
(
th
e
p
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ictio
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tem
)
is
th
en
co
n
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ec
ted
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ased
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r
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n
te
n
d
t
h
r
o
u
g
h
wh
ich
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s
er
s
will
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e
ab
le
to
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ter
ac
t
with
th
e
p
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io
n
s
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s
tem
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f
ec
tiv
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.
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h
e
f
r
o
n
ten
d
is
d
ev
elo
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e
d
with
PHP,
HT
ML
,
J
av
aScr
ip
t,
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d
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o
u
s
e
th
e
s
y
s
tem
u
s
er
s
ar
e
r
eq
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ir
e
d
to
h
a
v
e
r
eg
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te
r
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d
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g
in
s
u
cc
ess
f
u
lly
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to
th
e
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y
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tem
.
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f
th
is
is
n
o
t
s
atis
f
ied
,
u
s
er
s
ar
e
r
ed
ir
ec
ted
au
to
m
atica
lly
to
th
e
l
o
g
in
p
ag
e
(
Fig
u
r
e
1
0
)
.
T
h
e
r
e
g
is
tr
atio
n
an
d
lo
g
in
p
a
g
e
ar
e
d
esig
n
ed
s
u
c
h
th
at
in
p
u
t e
r
r
o
r
s
ar
e
h
a
n
d
led
.
T
h
e
f
o
r
m
o
n
Fig
u
r
e
1
1
is
r
eq
u
ir
ed
to
b
e
f
illed
b
y
th
e
u
s
er
to
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ak
e
a
p
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n
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h
is
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r
m
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s
d
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ch
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ain
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n
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n
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t b
o
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illed
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h
e
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d
ictio
n
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esu
lt
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is
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lay
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m
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al
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Fig
u
r
e
1
2
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iately
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f
ter
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ata
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s
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h
e
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s
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p
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ter
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e
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d
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e
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ir
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d
to
e
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d
with
“.
m
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r
“.
tx
t”.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
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n
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&
C
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m
p
Sci
,
Vo
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3
6
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3
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Dec
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b
er
20
24
:
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6
64
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6
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1670
Fig
u
r
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9
.
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d
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Fig
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Fig
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Pre
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
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4
7
52
P
r
ed
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a
n
a
lytics o
n
cro
p
yi
eld
u
s
in
g
s
u
p
ervis
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lea
r
n
in
g
tech
n
iq
u
es
(
Ju
liu
s
Ola
tu
n
ji Ok
eso
la
)
1671
5.
CO
NCLU
SI
O
N
T
h
is
p
r
o
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is
u
n
d
e
r
tak
en
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
an
d
to
e
v
alu
ates
th
e
p
er
f
o
r
m
a
n
ce
o
f
R
F,
SGD,
E
x
tr
aT
r
ee
r
eg
r
ess
o
r
(
E
T
)
,
Ad
aBo
o
s
t
r
eg
r
ess
o
r
,
a
n
d
l
in
ea
r
r
eg
r
ess
io
n
.
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n
th
e
d
ev
elo
p
ed
m
o
d
els,
am
o
n
g
all
th
e
f
iv
e
alg
o
r
ith
m
s
,
SGD
h
as
s
h
o
wn
g
r
ea
t
ab
ilit
y
in
p
r
ed
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g
th
e
y
ield
o
f
cr
o
p
s
co
m
p
a
r
ed
to
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th
er
m
o
d
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I
t
h
as
th
e
lo
west
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alu
e
o
f
er
r
o
r
s
an
d
h
ig
h
est
v
al
u
e
o
f
R
2
-
s
co
r
e.
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h
e
im
p
lem
en
tatio
n
o
f
th
is
s
y
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tem
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C
YPS)
will
aid
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th
e
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etter
m
en
t
o
f
th
is
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n
tr
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ag
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ltu
r
e
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r
ac
tices.
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m
ay
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e
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s
ed
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h
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ar
m
er
s
m
in
im
ize
th
eir
lo
s
s
es
an
d
in
cr
ea
s
e
cr
o
p
y
ield
s
in
o
r
d
er
to
in
cr
ea
s
e
th
eir
ca
p
ital
in
ag
r
icu
ltu
r
e.
T
o
aid
th
e
co
u
n
tr
y
’
s
ag
r
icu
ltu
r
al
p
r
o
g
r
ess
,
t
h
e
a
p
p
r
o
ac
h
m
ig
h
t
b
e
s
tr
en
g
th
en
e
d
b
y
in
teg
r
a
tin
g
it
with
o
th
e
r
s
ec
to
r
s
s
u
ch
as
h
o
r
ticu
ltu
r
e
,
s
er
icu
ltu
r
e,
cr
o
p
d
is
ea
s
e
p
r
ed
ictio
n
,
s
m
ar
t
ir
r
ig
atio
n
s
y
s
tem
,
cr
o
p
s
elec
tio
n
,
s
to
r
ag
e
s
y
s
tem
an
d
s
o
o
n
.
T
o
s
u
m
m
ar
ize,
th
is
r
esear
ch
h
as
th
e
p
o
ten
tial
to
tr
an
s
f
o
r
m
ag
r
ic
u
ltu
r
e
b
y
o
f
f
er
in
g
f
ar
m
er
s
,
p
o
li
cy
m
ak
er
s
,
an
d
o
th
e
r
s
tak
eh
o
ld
er
s
’
p
r
ac
tical
in
s
ig
h
t
s
th
at
w
ill
b
o
o
s
t
p
r
o
d
u
ctiv
ity
,
p
r
o
f
itab
ilit
y
,
an
d
r
esil
ien
ce
to
s
h
if
tin
g
m
ar
k
et
an
d
en
v
ir
o
n
m
en
tal
c
o
n
d
itio
n
s
as
it
s
co
n
tr
ib
u
ti
o
n
s
.
I
n
f
u
tu
r
e
,
f
ar
m
er
s
ca
n
b
e
em
p
o
wer
ed
to
m
a
k
e
p
r
o
m
p
t
an
d
well
-
in
f
o
r
m
e
d
d
ec
is
io
n
s
b
y
u
tili
zin
g
p
r
e
d
ictiv
e
an
aly
tics
to
d
e
v
elo
p
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
s
y
s
tem
s
an
d
d
ec
is
io
n
s
u
p
p
o
r
t
to
o
ls
.
I
n
o
r
d
er
t
o
s
u
p
p
o
r
t
ad
a
p
tiv
e
m
an
a
g
em
en
t
p
r
a
ctice
s
in
ag
r
icu
ltu
r
e,
f
u
tu
r
e
w
o
r
k
ca
n
co
n
ce
n
tr
ate
o
n
in
teg
r
atin
g
c
r
o
p
y
ield
p
r
ed
ictio
n
s
with
p
r
ac
tical
r
ec
o
m
m
en
d
atio
n
s
,
au
to
m
ated
ale
r
ts
,
an
d
in
ter
ac
tiv
e
in
ter
f
ac
e
s.
ACK
NO
WL
E
DG
E
M
E
NT
T
h
e
APC
wa
s
f
u
n
d
ed
b
y
th
e
Af
r
ica
C
en
tr
e
o
f
E
x
ce
llen
ce
o
n
T
ec
h
n
o
l
o
g
y
E
n
h
an
ce
d
L
ea
r
n
in
g
(
AC
E
T
E
L
)
,
Natio
n
al
Op
en
U
n
iv
er
s
ity
,
Ab
u
ja,
Nig
e
r
ia
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
O
.
A
r
o
w
o
l
o
,
M
.
O
.
A
d
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a
n
d
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.
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m
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i
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a
.
v
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9
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1
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1
6
3
8
1
.
[
2
]
M
.
O
.
A
r
o
w
o
l
o
,
M
.
O
.
A
d
e
b
i
y
i
,
A
.
A
.
A
d
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b
i
y
i
,
a
n
d
O
.
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O
k
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so
l
a
,
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r
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c
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n
g
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S
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q
d
a
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m
a
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n
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mb
l
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ss
i
f
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c
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t
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h
m
s,”
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n
d
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s
i
a
n
J
o
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a
l
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l
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-
1
0
8
1
.
[
3
]
F
.
B
a
u
d
r
o
n
,
M
.
A
.
Za
m
a
n
-
A
l
l
a
h
,
I
.
C
h
a
i
p
a
,
N
.
C
h
a
r
i
,
a
n
d
P
.
C
h
i
n
w
a
d
a
,
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U
n
d
e
r
st
a
n
d
i
n
g
t
h
e
f
a
c
t
o
r
s
i
n
f
l
u
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n
c
i
n
g
f
a
l
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a
r
m
y
w
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m
(
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p
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a
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.
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)
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sma
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mb
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p
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2
8
.
[
4
]
S
.
A
.
A
j
a
g
b
e
a
n
d
M
.
O
.
A
d
i
g
u
n
,
“
D
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
d
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t
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c
t
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o
n
a
n
d
p
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d
i
c
t
i
o
n
o
f
p
a
n
d
e
m
i
c
d
i
se
a
ses
:
a
s
y
s
t
e
ma
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
M
u
l
t
i
m
e
d
i
a
T
o
o
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s
a
n
d
A
p
p
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s
,
v
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l
.
8
3
,
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,
p
p
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2
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2
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,
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o
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/
s
1
1
0
4
2
-
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-
1
5
8
0
5
-
z.
[
5
]
N
.
B
r
i
ss
o
n
,
P
.
G
a
t
e
,
D
.
G
o
u
a
c
h
e
,
G
.
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h
a
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t
,
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.
-
X
.
O
u
r
y
,
a
n
d
F
.
H
u
a
r
d
,
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h
y
a
r
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w
h
e
a
t
y
i
e
l
d
s
st
a
g
n
a
t
i
n
g
i
n
E
u
r
o
p
e
?
A
c
o
mp
r
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h
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n
s
i
v
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d
a
t
a
a
n
a
l
y
si
s
f
o
r
F
r
a
n
c
e
,
”
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i
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l
d
C
ro
p
s
R
e
se
a
rc
h
,
v
o
l
.
1
1
9
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n
o
.
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,
p
p
.
2
0
1
–
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1
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,
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c
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j
.
f
c
r
.
2
0
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0
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0
7
.
0
1
2
.
[
6
]
M
.
S
.
K
u
k
a
l
a
n
d
S
.
I
r
mak
,
“
C
l
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ma
t
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-
d
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v
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c
r
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p
y
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d
a
n
d
y
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v
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d
c
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c
h
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U
.
S
.
g
r
e
a
t
p
l
a
i
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s
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g
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c
u
l
t
u
r
a
l
p
r
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d
u
c
t
i
o
n
,
”
S
c
i
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n
t
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f
i
c
Re
p
o
r
t
s
,
v
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l
.
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,
p
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3
8
/
s
4
1
5
9
8
-
0
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8
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2
1
8
4
8
-
2.
[
7
]
M
.
O
.
A
d
e
b
i
y
i
,
O
.
O
.
A
d
e
o
y
e
,
R
.
O
.
O
g
u
n
d
o
k
u
n
,
O
.
J.
O
l
a
t
u
n
j
i
,
a
n
d
A
.
A
.
A
d
e
b
i
y
i
,
“
S
e
c
u
r
e
d
l
o
a
n
p
r
e
d
i
c
t
i
o
n
s
y
st
e
m
u
si
n
g
a
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
J
o
u
r
n
a
l
o
f
E
n
g
i
n
e
e
r
i
n
g
S
c
i
e
n
c
e
a
n
d
T
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h
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o
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o
g
y
,
v
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l
.
1
7
,
n
o
.
2
,
p
p
.
8
5
4
–
8
7
3
,
2
0
2
2
.
[
8
]
I
.
Ew
e
o
y
a
,
A
.
A
.
A
y
o
d
e
l
e
,
A
.
A
z
e
t
a
,
a
n
d
O
.
O
l
a
t
u
n
j
i
,
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F
r
a
u
d
p
r
e
d
i
c
t
i
o
n
i
n
b
a
n
k
c
r
e
d
i
t
a
d
mi
n
i
s
t
r
a
t
i
o
n
:
a
s
y
s
t
e
mat
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
J
o
u
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n
a
l
o
f
T
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re
t
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a
l
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n
d
A
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n
f
o
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a
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T
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y
,
v
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l
.
9
7
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n
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.
1
1
,
p
p
.
3
1
3
5
–
3
1
5
7
,
2
0
1
9
.
[
9
]
S
.
V
e
e
n
a
d
h
a
r
i
,
B
.
M
i
sr
a
,
a
n
d
C
.
S
i
n
g
h
,
“
M
a
c
h
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n
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g
a
p
p
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c
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f
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a
st
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c
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d
b
a
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d
o
n
c
l
i
m
a
t
i
c
p
a
r
a
m
e
t
e
r
s,”
i
n
2
0
1
4
I
n
t
e
r
n
a
t
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o
n
a
l
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o
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c
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m
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d
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s
,
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n
.
2
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4
,
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.
1
–
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,
d
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:
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1
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/
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C
C
C
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.
2
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.
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2
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7
1
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.
[
1
0
]
T.
V
a
n
K
l
o
m
p
e
n
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a
d
a
n
.
S
h
e
is
a
lec
tu
re
r
a
n
d
fa
c
il
it
a
t
o
r
in
t
h
e
D
e
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
Na
ti
o
n
a
l
Op
e
n
U
n
iv
e
rsit
y
o
f
Ni
g
e
ria
(NO
UN
),
a
n
d
a
lso
a
fa
c
il
it
a
to
r
a
t
th
e
Afric
a
Ce
n
tre
o
f
Ex
c
e
ll
e
n
c
e
o
n
Tec
h
n
o
lo
g
y
En
h
a
n
c
e
d
Lea
r
n
in
g
(ACE
TE
L
-
NO
UN
).
Re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
fo
re
n
sic
sc
ien
c
e
,
d
a
ta
a
n
a
ly
ti
c
s,
p
a
tt
e
r
n
re
c
o
g
n
it
i
o
n
,
AI
a
n
d
d
a
ta
sc
ien
c
e
a
n
d
sh
e
h
a
s
a
n
u
m
b
e
r
o
f
p
u
b
li
c
a
ti
o
n
s
i
n
th
e
se
field
s
th
a
t
h
a
v
e
a
p
p
e
a
re
d
in
h
ig
h
imp
a
c
t
jo
u
r
n
a
ls
a
n
d
lea
rn
e
d
c
o
n
f
e
re
n
c
e
s.
S
h
e
is
a
m
e
m
b
e
r
o
f
m
a
n
y
a
ss
o
c
iati
o
n
s,
a
m
o
n
g
wh
ich
a
re
th
e
Org
a
n
iza
ti
o
n
o
f
W
o
m
e
n
in
S
c
ien
c
e
fo
r
t
h
e
De
v
e
lo
p
in
g
W
o
rl
d
,
Blac
k
-
in
-
AI
a
m
o
n
g
st
o
th
e
rs.
S
h
e
is
t
h
e
m
o
n
it
o
rin
g
a
n
d
e
v
a
lu
a
ti
o
n
o
ffice
r
fo
r
Afr
ica
Ce
n
tre
o
f
E
x
c
e
ll
e
n
c
e
o
n
Tec
h
n
o
l
o
g
y
E
n
h
a
n
c
e
d
Le
a
rn
in
g
,
NO
UN
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
a
b
io
d
u
n
@n
o
u
n
.
e
d
u
.
n
g
.
Fra
n
c
is
Bu
k
ie
O
sa
n
g
,
Ph
.
D
.
o
b
tain
e
d
h
is
P
h
.
D
.
fr
o
m
th
e
I
CT
Un
i
v
e
rsity
Ya
o
u
n
d
é
Ca
m
e
ro
o
n
in
2
0
1
6
.
He
i
s
a
se
n
io
r
lec
tu
re
r
i
n
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
Na
ti
o
n
a
l
Op
e
n
Un
i
v
e
rsity
o
f
Ni
g
e
ria.
He
h
a
s
a
u
th
o
re
d
o
v
e
r
se
v
e
n
ty
(
7
0
)
p
u
b
li
c
a
ti
o
n
s
i
n
re
fe
rre
d
in
tern
a
ti
o
n
a
l
a
n
d
n
a
ti
o
n
a
l
jo
u
rn
a
ls
a
n
d
c
o
n
fe
re
n
c
e
p
r
o
c
e
e
d
in
g
s.
His
re
se
a
rc
h
i
n
tere
sts
in
c
l
u
d
e
in
f
o
rm
a
ti
o
n
s
y
st
e
m
s
o
p
ti
m
iza
ti
o
n
/se
c
u
rit
y
,
h
u
m
a
n
-
c
o
m
p
u
ter
i
n
tera
c
ti
o
n
s,
sm
a
rt
c
o
m
p
u
ti
n
g
,
c
y
b
e
r
-
se
c
u
rit
y
a
n
d
e
Lea
rn
in
g
.
He
h
a
s
wo
n
se
v
e
ra
l
g
ra
n
ts
fo
r
re
se
a
rc
h
.
He
is
a
F
e
ll
o
w
o
f
th
e
Ni
g
e
ria
Co
m
p
u
ter
S
o
c
iet
y
(F
NCS).
He
is
a
lso
a
m
e
m
b
e
r
o
f
Co
m
p
u
ter
P
ro
fe
ss
io
n
a
ls
Re
g
istratio
n
Co
u
n
c
il
o
f
Nig
e
ria
(CP
N)
a
n
d
I
n
tern
a
ti
o
n
a
l
P
ro
fe
ss
io
n
a
l
M
a
n
a
g
e
rs As
so
c
iatio
n
,
UK
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
fo
sa
n
g
@n
o
u
n
.
e
d
u
.
n
g
.
O
la
k
u
n
le
O
.
S
o
l
a
n
k
e
,
Ph
.
D
.
re
c
e
iv
e
d
h
is
P
h
.
D.
fr
o
m
th
e
F
e
d
e
ra
l
Un
iv
e
rsity
o
f
Tec
h
n
o
l
o
g
y
,
Ak
u
re
,
Nig
e
ria
in
2
0
1
6
.
He
is
a
lec
tu
re
r
in
th
e
De
p
a
rtme
n
t
o
f
M
a
th
e
m
a
ti
c
a
l
S
c
ien
c
e
s
a
t
Ola
b
isi
On
a
b
a
n
jo
Un
iv
e
rsity
,
Nig
e
ria.
He
h
a
s
a
u
t
h
o
re
d
m
o
re
th
a
n
3
0
a
rti
c
les
.
He
h
a
s
b
e
e
n
in
v
o
lv
e
d
in
re
se
a
r
c
h
a
c
ti
v
it
ies
i
n
t
h
e
a
re
a
o
f
n
e
tw
o
rk
i
n
tr
u
sio
n
d
e
tec
ti
o
n
,
n
e
two
rk
c
o
n
g
e
stio
n
c
o
n
tr
o
l,
in
f
o
r
m
a
ti
o
n
se
c
u
rit
y
,
a
n
d
m
a
c
h
in
e
lea
rn
in
g
.
He
is a me
m
b
e
r
o
f
th
e
Ni
g
e
rian
C
o
m
p
u
ter
S
o
c
iety
a
n
d
th
e
IEE
E.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
so
lan
k
e
.
o
lak
u
n
le@
o
o
u
a
g
o
iwo
y
e
.
e
d
u
.
n
g
.
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