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0517-6611(2017)25-0199-04
PredictiveClassificationofCultivatedLandatCountyScaleUsingFisherDiscriminantAnalysisAlgorithms—ACaseStudyofHuixianCity,HenanProvinceWANGHaiyang,CHENJie,HANXingxingetal (SchoolofEnvironmentandWaterConservancy,ZhengzhouUniversity,Zhengzhou,Henan450001)Abstract [Objective]Tosimplifytheevaluationofcultivatedlandfertilitybyapplyingthemachinelearningalgorithm,whichaimstoexploreanewapproachtotheapplicationofmachinelearningmethodintheevaluationworkofcultivatedlandfertilityatregionalscale.[Method]BasedonTechnicalSpecificationforInvestigationandQualityEvaluationofCultivatedLandFertility(NY/T1634—2008)andthelocalpracticesofcultivatedlandevaluation,themethodsappliedbythisstudygenerallyaresupposedtousethebaseddataobtainedbythefinancialsubsidyprojectforsoiltestingandformulatedfertilizationconductedinHuixianCity,HenanProvince,toestablishcanonicaldiscriminatefunctions.10soilandsiteconditionfactorsincludingsurfacesoiltexture,soilprofilecharacteristics,surfacegraveldegree,rapidlyavailablepotassiuminsoil,availablephosphorousinsoil,organicmattercontentinsoil,irrigationguaranteerate,capacityfordrainage,geomorphictypes,andsurfaceslopeareselectedasthediscriminantvariablesofcultivatedlandfertilitylevel.ByconstructingthemodelofFisherdiscriminantfunctions,Fisherdiscriminantanalysis(FDA)wasemployedtodetermine,analyzedandclassifiedlandfertilityin5922sampledsitesofthestudiedregionusingthatFisherdiscriminatefunctions.[Result]Theresultsofthemethodsdemonstrateapredictionaccuracyreachingup91.4% aftermathematicalstatisticsverificationandbacksubstitutionverificationwhichmeanstheoriginaldatabeingreturnedbacktotheFisherdiscriminantfunctions.[Conclusion]Underthepremiseofidentifyingthestandardofevaluationandclassificationofcultivatedland,thediscriminantanalysisalgorithmhasauniqueadvantageinanalyzingandclassifyingthefertilitysituationofcultivatedlandandpredictingthegradeofcultivatedland.Keywords Cultivatedlandfertility;Evaluationofcultivatedlandfertility;Discriminantanalysis;Canonicaldiscriminatefunctions
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,JournalofAnhuiAgri.Sci.2017,45(25):199-202,252
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Table1 TheresultsoflandfertilityclassificationandnutrientstatusofcultivatedlandinHuixianCity
34¾¿
Cultivatedgrade [
Area∥hm2îf
Proportion∥%úöJÙK
Organicmattercontent∥g/kg
úµØÙK
Effectivephosphoruscontent∥mg/kg
Öµ×ÙK
Availablepotassiumcontent∥mg/kg
L¾4
Grade1 21230 35.38 24.43 21.03 179.91®¾4
Grade2 20500 34.17 20.90 18.82 136.77ï¾4
Grade3 15110 25.19 21.27 21.73 126.82ð¾4
Grade4 3160 5.27 22.09 23.28 118.37fñ
Mean — — 22.17 21.21 139.95
2.3 YZ[(
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(j)ip,i=1,…,nj (2)
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珋y(j)=1n1(y(j)1 +…+y
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珋y(1)>珋y(2),æ�ç
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2 k>lm)#$nToT
Table2 Nonstandardizeddiscriminantfunctioncoefficients
ÞK
Variable
1ÈÉéê
Firstdiscriminantfunction
2ÈÉéê
Seconddiscriminantfunction
3ÈÉéê
Thirddiscriminantfunction
x1 4.241 -0.469 2.571x2 5.353 -2.440 -1.024x3 0.971 4.686 -0.801x4 1.151 0.054 1.083x5 2.302 -0.925 4.606x6 3.329 -2.451 5.083x7 3.134 0.540 1.745x8 2.385 2.381 0.418x9 4.459 -1.665 -1.142x10 1.838 4.050 1.243C -21.160 -4.420 -10.604
B�6eÄRFeGÑ
3M&æÈÉéêûü
:
1ÈÉéê
:
y1=4.241x1+5.353x2+0.971x3+1.151x4+2.302x5+3.329x6+3.134x7+2.385x8+4.459x9+1.838x10-21.600 (9)
2ÈÉéê
:
y2=-4.469x1-2.440x2+4.686x3+0.054x4-0.925x5-2.451x6+0.540x7+2.381x8-1.665x9+4.050x10-4.420 (10)
3ÈÉéê
:
y3=2.571x1-1.024x2-0.801x3+1.083x4+4.606x5+5.083x6+1.745x7+0.418x8-1.142x9+1.243x10-10.604 (11)
FisherÈÉéê@íê9H�·þ@\ÁÞK¹±M
ÈÉéê@IJÝ
,e
29H�
:./Ó ÕÖ'
1ÈÉ
éêNÉW=ª
,4eÔÕA'
2ÈÉéêNÉW=ª
,
'
3ÈÉéêNÉ@5ØK@./½4eVWÞK
。e
35¹
3Méê@
Wilks’LamdbaL/
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e
3<
,L/@ã!��±ýÞKñ×N¾
,Wilks’Lambda
5ýRfàX'9fàX@î
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,×h
O
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;Pà5
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;Sig.Hs
0.05,eâ
3MÈÉéêñæúrß
$R@RS
。
_
3 Wilks’Lambdaij
Table3 Wilks’Lambdatest
éêL/
Functiontest Wilks’Lambda Pà
Kappa df Sig.
1~3 0.056 17090.510 30 0.0002~3 0.698 2126.624 18 0.0003 0.933 409.737 8 0.000
FisherÈÉÀÊ<
,&æÈÉéê@»TKB�`Øà
E7U@îfu»T
(e
4)。
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4 #$nT[pdqrs
Table4 Thevarianceofdiscriminantequationanditssignificance
éê
FunctionÕÖ×
Eigenvalues
àEIJÝ
Variancecontributionrate∥%
ZßàEIJÝ
Cumulativevariancecontributionrate∥%
&æNÉíê
Canonicalcorrelationcoefficient
1 11.557 96.6 96.6 0.9592 0.337 2.8 99.4 0.5023 0.072 0.6 100.0 0.259
ìe
4B�V�
,
1ÈÉéêàE7Uîf�
966%,WâØB�»Tëp
96.6%?@
,X&`ðéêK
BÐѹY#�Àë/@ÈÉ
,Z&`
1ÈÉéê[á¹
ë/7VZÉT�â¼È�ä
,\�
2]ª
3ÈÉéê
u»T7úëâ@?@
。�
1、
2&æÈÉéê��Q
^�ç_OÀÃÎ
(Î
1)。
9
1 !"tu#$nT),--./0vw%x
Fig.1 Thejointdistributionofcultivatedlandgradeusingdis
criminantfunctions
Î
15ÎÏ
1X
2&æÈÉéêT�@`/Î
,Î
RãG
,4MZÉNãM4aðxɸ
。ÑÒxë/±ÞK
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2&æÈÉéê<
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Table5 Theresultsofdiscriminantanalysisforcultivatedlandfertili
tyinthestudyarea
34¾¿
CultivatedgradeL¾4
Grade1®¾4
Grade2ï¾4
Grade3ð¾4
Grade4Oß
TotalL¾4
Grade1 762 7 0 0 769®¾4
Grade2 155 1554 118 0 1827ï¾4
Grade3 0 25 1602 52 1679ð¾4
Grade4 0 0 154 1493 1647Oß
Total 917 1586 1874 1545 5922
9
2 `*a,--./0
Fig.2 FertilityclassificationofcultivatedlandinHuixianCity
3.2 -./0#$bcij
¹ÑÒx344G¾¿
Fisher
Èɲ³@L/Y`PàL/Xm�áJÔàá
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,~�rß²
³@ãMWÀÊ�(Hx
。!�ëâ~�ÈÉÀÊéêC
í@ÀZ²³þw#pÁ@²³ôúEÉ
,<qLrr
ßK
:
χ2=[N-(o×g)]N(g-1) (12)
ü<
,N�ëâ9ê
;g�ýê
;o��¼ÀZ@#Á×
。ê
ØsìT5A�
1@PàÀÃ
,7�Ø×8#s
3.84(α=005)
è
6.64(α=0.01),eGEsÈÉÀÊ�£ÆÇ@²³
XtöuÁ@²³úãM·þ
。ÑÒxë/9ê
N=5922,344G¾¿ê
g=4,EsÈÉéêÆC�¼ÀZ@ë/ê
o=5410,Lr@rßKv#s
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Table6 Thetestofdiscriminantresultsofcultivatedlandfertility
²�
Project4G¾¿
Fertilitygrade
\Á¾¿
Forecastgrade
1 2 3 4Oß
Total
ë/ê
1 762 7 0 0 769Numberof 2 155 1553 119 0 1827samplepoint 3 0 25 1602 52 1679
4 0 0 154 1493 1647îf
1 99.1 0.9 0 0 100Proportion∥% 2 8.5 85.0 6.5 0 100
3 0.0 1.5 95.4 3.1 1004 0.0 0.0 9.4 90.6 100
Scùò`<
,xB�Y`m�áßïÈÉiÙ@mÈ
Ýηu�(iÙL/
:
η=ȹëâê
n (13)
L1η>75%Ky�ÈÉiÙúµ
。�ë/êÏm�
ÈÉiÙ
,CmÈÝ η=5410/5922=91.35%,zÈÝó
�
,WâðiÙdNS4ò`s344G©;@È�ÀÊX
ÚZÀ¿
。
4 by
(1)+,-���@efÑÒeâ
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、�µ
、��@Õ/�ÈÉÀÊáSÝPQ
、
ÝêëÀZír@ëâÚVÈ�opæú�#@ò`�G
。
(2)ÈÉÀÊïá5SÀZ¼Ë@ÌÍa
,ÎÏëâ@
±ÕÖ×ÈÉØÀZÚV@LÔÝÞKrßÀÊàá
。ê
ð
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