5
!" Fisher #$%&'()*+,--./012———!"#$%&'() *+, -. /00 123 1. !"#$%&'()$*+,!" 450001 2. +,-./012+,!" 450002 34 456789:;<=———>?@A<=BCDEFFGHIJKLMNOPQR89:;S=TFGHIU75VW X。[ S=YZ%&'[\]S^_`abcd4EFFGHIJKef5YghijiklmnopqEFFGrstuvH Iwxy1》( NY/T1634 2008 z{'EFFGHI|}~fN\uF\FQ 8uvGFQ 10 \z F¡¢£¤K(EFFG¥¦5>?§v¨© Fisher ª«> ?¬h-® 5922 HI¯°5EFFG±²³´>µ@Az¶@·。[ ¸¹~®>?¸¹³´º»z¼½¾¿À [>?ÁÂÃÄ 91.4%。[ ¸ÅTEFFGHIt@·pqÂÆ5ÇÈÉ>?@A<=TNOPQR®@AEFFG±²À[ EFFG·SÊËÌÍ567 EFFGEFFGHI>?@Aª«>?¬h 89%:; S158  <=>?@ A  <AB; 0517-6611 2017 25-0199-04 PredictiveClassificationofCultivatedLandatCountyScaleUsingFisherDiscriminantAnalysisAlgorithms ACaseStudyofHuix ianCity HenanProvince WANGHaiyang CHENJie HANXingxingetal SchoolofEnvironmentandWaterConservancy ZhengzhouUniversity Zheng zhou Henan450001 Abstract Objective Tosimplifytheevaluationofcultivatedlandfertilitybyapplyingthemachinelearningalgorithm whichaimstoexplore anewapproachtotheapplicationofmachinelearningmethodintheevaluationworkofcultivatedlandfertilityatregionalscale. Method Basedon TechnicalSpecificationforInvestigationandQualityEvaluationofCultivatedLandFertility NY/T1634 2008 andthelocalprac ticesofcultivatedlandevaluation themethodsappliedbythisstudygenerallyaresupposedtousethebaseddataobtainedbythefinancial subsidyprojectforsoiltestingandformulatedfertilizationconductedinHuixianCity HenanProvince toestablishcanonicaldiscriminatefunc tions.10soilandsiteconditionfactorsincludingsurfacesoiltexture soilprofilecharacteristics surfacegraveldegree rapidlyavailablepo tassiuminsoil availablephosphorousinsoil organicmattercontentinsoil irrigationguaranteerate capacityfordrainage geomorphic types andsurfaceslopeareselectedasthediscriminantvariablesofcultivatedlandfertilitylevel.ByconstructingthemodelofFisherdiscrim inantfunctions Fisherdiscriminantanalysis FDA wasemployedtodetermine analyzedandclassifiedlandfertilityin5922sampledsitesof thestudiedregionusingthatFisherdiscriminatefunctions. Result Theresultsofthemethodsdemonstrateapredictionaccuracyreachingup 91.4%aftermathematicalstatisticsverificationandbacksubstitutionverificationwhichmeanstheoriginaldatabeingreturnedbacktothe Fisherdiscriminantfunctions. Conclusion Underthepremiseofidentifyingthestandardofevaluationandclassificationofcultivatedland thediscriminantanalysisalgorithmhasauniqueadvantageinanalyzingandclassifyingthefertilitysituationofcultivatedlandandpredicting thegradeofcultivatedland. Keywords Cultivatedlandfertility Evaluationofcultivatedlandfertility Discriminantanalysis Canonicaldiscriminatefunctions !CDE lÎÏÐÑ:YÒd440971128 )。 FGHI *+,1993 —), Ó"#ÔÕÖ×ØÙ\ÛÜÀ[\FÝÞHIßàKáâãäå\ FÝÞæçJKLM 2017-06-21 3456789:;<=>?@89A156789 BCD@EF 344G5HI34JK@LMNOP Q 534RSTUVWXYZ[\:;]^_`a@b c89dGeb344G%fghijk_l8mno69p9KXJK +,-5qr@67#-5st= >?@uvw9xyLz{k|}t~uv@>? cbt~uvx》《 +,-Qu @Q-C3 4JK c¡34JK¢ NO£¤¡¥¦34./§¨W©34NO89 dGª¨«¬TU-®@dG|¯678°()±² 34JK³oNO4G¢£¤¡@´&µc¡·¸¹3489Gb©@ º»¼HI½¹344 G¾¿@$ÀXÁÂÀÃÄÅ@ ÆÇÈÉÀÊDiscriminateAnalysis DA 5SÀZ¼Ë@Ì ÍaÎÏÐLÑÒ¹Ó@±ÔÕÖ×ÈÉØZÙÚVÛÜ @LÔÝÞKrßÀÊàáØEâã§5äåLË@ÈÉ æçLMèÝMÈÉéê`ÑÒ¹Ó@#ëâêÏ ¼ËÈÉéê<@ìËíêîßïÈÉPQÏð¼ËÕ Ëëâ@ZÙÚV 4-6 ÈÉÀÊ5LÔScñ<ò`ó ôõ@ö÷$øïá cùò`<ÈÉÀÊúÝÔû üÎÏÈÉüý§ÞK@àá·þBÀÿÈÉ! "ÈɾÎÏÈÉQ·þ#¶ÈÉBayes È ÉFisher ÈÉá¾ Fisher ÈÉÀÊFisherDiscriminant Analysis FDA $%&æÈÉ5ÎÏ'W Fisher éê×( ÈÉØEâ)*+5,i-¹ .ÁÂ<@Ð/ ,…, ), 01LMd23L.ê×@'Wéê U4ò`5M'Wéê6 .ÁÂ<@789:½;8ZÉÚV@ëâ<Þ=L.êÏ>ÎÏØ Â@?@A6B8ÚV@ëâ/ÈËØÚV,i@ã æ5CDLZ@EFGBdHI·þZÂ,i@¶EG Bd#ÈÉéêw?úJÔK'WÈÉéêLinearDis criminantFunction X&æÈÉéêCanonicalDiscriminate Function )。Ø<L'WÈÉéê@EâM5±Zëâ N^¹çOPO°ÀÃI&æÈÉéê5çSàE ÀÊ)QRS4xÀ±M9:I·¹9:ÀÃT UV?; 9-10 st67(7QNY/T1634 2008 < NOPQRSJournalofAnhuiAgri.Sci.2017 45 25 ): 199-202 252

Fisher#$%&’()*+,--./012 · 书!" Fisher#$%&’()*+,--./012———!"#$%&’() *+,1,- .1,/001,1232 (1.!"#$%&’()$*,+,!" 450001;2.+,-./012,+,!" 450002) 34 [45]6789

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Page 1: Fisher#$%&’()*+,--./012 · 书!" Fisher#$%&’()*+,--./012———!"#$%&’() *+,1,- .1,/001,1232 (1.!"#$%&’()$*,+,!" 450001;2.+,-./012,+,!" 450002) 34 [45]6789

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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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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[(

 ÈÉÀÊ@Eâã§5äåLË@ÈÉ�

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k=1ckx

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Page 3: Fisher#$%&’()*+,--./012 · 书!" Fisher#$%&’()*+,--./012———!"#$%&’() *+,1,- .1,/001,1232 (1.!"#$%&’()$*,+,!" 450001;2.+,-./012,+,!" 450002) 34 [45]6789

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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/

e

3<

,L/@ã!��±ýÞKñ×N¾

,Wilks’Lambda

5ýRfàX'9fàX@î

,L/ÈÉéê@ã

MW%f

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0~1,×'HeGýÂúN#@EF

,×h

O

1eGôúýÂEF

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0.05,eâ

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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

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Variancecontributionrate∥%

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Cumulativevariancecontributionrate∥%

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[6]CHENL,ZOULJ,TUL.Streamdataclassificationusingimprovedfisherdiscriminateanalysis[J].Journalofcomputers,2009,4(3):208-214.

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