analysis of remote sensed data using different image processing algorithms applied to agriculture...

7/24/2019 Analysis of Remote Sensed Data using Different Image Processing Algorithms applied to Agriculture Yield Optimiza

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International Journal of Advance Foundation and Research in Computer (IJAFRC)

Volume 2, Issue 9, Septemer ! 2"#$%ISS& 2' * $', Impact Factor * #%'#+

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Anal1sis of Remote Sensed ata usin. ifferent Ima.e

3rocessin. Al.orithms applied to A.riculture 4ield

5ptimi6ation 7odel8 A Case Stud1 of areill1 Re.ion:ari ;umar Sin.h


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)and use is affected '! changes in land coer as (ell as land coer is affected '! land use. It is notnecessar! that the change in an!one (ould sere as product for the other one. )and coer ma! 'e changeas a result of )and use 'ut it is not designated as the (astage of land. *ometimes, land coer changestakes place due to seeral changes= shifting in land use due to social regions affects seeral processesaffecting 'iosphere and climate like/ radiation < (ater 'udget, 'iodiersit!, trace gas emission etc.4Rie'same, Me!er, and Turner, 5;;?7

@iaomei :, and Rong Aing ).A.: in 5;;; told that for update the maps related to land coer and fornatural resource management, this change related information is necessar!. The source of thisinformation ma! 'e remote sensed data eBtraction or ground site isits.

The identification in difference of the state of a phenomenon or o'0ect '! o'seration them at seeraltime interals is kno(n as Change detection 4*ingh, 5;;7. In Change detection (e can perform1uantitatie anal!sis for population3s spatial distri'ution hence it ma! 'e effectiel! used for ur'andeelopment, managing < monitoring the natural resources.

*hosheng and utiel in 5;;? esta'lished that the techni1ues of remote sensing are adantageous for

egetation coer details in a region, in respect to sure!s in fields. Their research results (ere utili2ed toform four maps related to egetation coerage (hich further utili2ed to gather ne( information oftemporal and spatial distri'ution related to egetation in the related area and ena'led the egetationcoer assessment 1uantitatiel! in the region.

+rind C. Pand! and M. *. atha(at in FGGH perform a stud! 'ased on mapping of land coer and landuse in +m'ala, Panchkula and :amunanager districts in 9ar!ana, India. Their stud! recall that differentt!pe of land coer land use deelopments takes place in those districts due to heterogeneousph!siographic < climate conditions, maBimum of the area of these districts is used for the purpose ofagriculture as reealed '! digital anal!sis ealuation performed upon remote sensed data receied '!satellite. In hill! areas, considera'le resered forests deelopment took place. The controlling of landcoer land use pattern is done in these areas '! the factors like/ ground (ater potential, climate

conditions, other factors etc. 45H7The *atellite Data Image, arranged from R*+ #ational Remote *ensing +genc!, *pace Department,oernment of India$ integrates *patial < *pectral features of o'0ects related to satellite imageprocessing. In this (ork, spectral signature are eBtracted for seeral o'0ects in "areill! region (ith theuse of remote sensing data in multispectral form, for o'0ecties like/ the classification of land coer,change in land use in time frame, anal!sis of climate impact upon surface through temporal anal!sis4H,F7. In our stud! (e achiee follo(ing o'0ecties

#i$Multi spectral image anal!sis in different 'end.#ii$ To determine DKI3s threshold alue for the o'0ect under classification from sure! data done atground.

#iii$ To create alse Colour composite image of the o'0ects under classification like/ structures,egetation, free land, roads and (ater.#i$ Calculation of transformed image using D-T.#$ Calculation of transformed image using DCT.#i$ Calculation of comparatie chart for different algorithms of digital image processing.#ii$ +nal!2ed different data for !ield optimi2ation.#iii$ Mathematical modelling for optimi2ed !ield.

II% RE75=E SE&SI&

In Remote *ensing (e eBtract information related to an o'0ect '! the anal!sis of data of o'0ect sensed

remotel!. There are three parts in it, irst is Targets/ Phenomena or o'0ect related to an area *econd isData +c1uisition/ (ith the use of some instruments Third is Data +nal!sis/ performed using somedeices. Remote sensing includes seeral s!stems including/ sonar sounding in sea ision s!stem likehuman e!e medical science applications as B/ra! and ultrasound atmospheric particles laser pro'ing.


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The si2e of target ma! 'e of an! range starting from 'iological cell to planets like earth, moon etc. Thisprocess is descri'ed as follo(s 457.

igure. 5. Remote *ensing #lo(s of Energ! and Information$

The instruments related to remote sensing ma! 'e placed at seeral platforms for imaging and ie(

targets. The platform ma! 'e ground 'ased, aircraft 'ased or satellite 'ased like toda!s. There are seeralcharacteristics haing uni1ue features (ith satellites hence the! are er! useful in the applications ofremote sensing for the surface of the Earth. 4F, F?, FL7

III% AR=IFICIA@ &EDRA@ &E=H5R;

It is the ne( 'ranch of +rtificial Intelligence and also kno(n as eural et(orks. In +rtificial euralet(ork human 'rain simulation is done through electronics or soft(are. 9uman 'rain has theproperties to anal!sis the compleB patterns containing seeral elements. These patterns cannot full!descri'e a'out the total pattern 'ut (ith com'ination the! represent o'0ects (hich ma! 'e recogni2ed.

igure F. + eural et(ork Model

The concept of +rtificial eural et(ork is deried from human 'rain as human 'rain performs indifferent (a! as of toda!3s computers. This method is inspired from 'iolog! and (ill 'e (idel! used in

future for computing as it relies the programmer from making algorithms and long programs.

+rtificial eural et(ork is computational or mathematical model (hich is used to simulate functional orstructural aspect of neural net(ork as in 'iolog!. + includes a group of artificial neurons hainginterconnections and it eBecutes the information (ith the use of connection oriented method forcomputation 45H7.

IV% C5&=I&D5DS HAVE@E= =RA&SF5R7

The C-T #Continuous -aelet Transform$ ma! 'e calculated using the follo(ing e1uation. In thise1uation B#t$ represents anal!2ed signal, #t$ represents the 'asic function also kno(n as mother

(aelet. Mother (aelet is used to derie all (aelet functions (hich are used in this transformation,using translation like/ scaling or shifting.

/t ( ,s ) K#B(

s

)

?(t)%L((t! )Bs)dt


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Mother (aelet, (hich is used for generation of all 'asic functions, is carefull! designed such that it isdepended upon function3s desired characteristics. Translation parameter NO3 is related to (aeletfunction3s location and shifted (ith the signal. 9ence it is related to the time related information in(aelet transform 47.

V% ISCRE=E C5SI&E =RA&SF5R7 (C=)The DCT #Discrete cosine transform$ is used to conert a signal into its components of elementar!fre1uenc!. Image compression is a ma0or application of it. DCT is computed of function as follo(s. TheDCT is a close relatie of the discrete ourier transform #DT$. The one/dimensional DCT is useful inprocessing one/dimensional signals such as speech (aeforms. or anal!sis of t(o/dimensional #FD$signals such as images, (e need a FD ersion of the Discrete Cosine Transform.

The DCT #Discrete Cosine Transform$ is used to separate an image into spectral su' 'ands haingdifferent alues in respect to isual 1ualit! of image. DCT ma! 'e compared to DT #Discrete ourierTransform$ as it is used for transformation from spatial to fre1uenc! domain 4FG7.

ig. 6. Transformation of image from the spatial domain to the fre1uenc! domain.

VI% &5R7A@IME IFFERE&CE VEE=A=I5& I&EN

It is possi'le to monitor egetation densit! '! means of a measure or indeB 45745;74FG7. This measure,

or egetation indeB, makes use of the difference in spectral reflection of green egetation in the nearinfrared #IR$ and the red parts of the spectrum #RED$. The reflectance is recorded as digital num'ers inthe arious 'ands or channels of satellite sensors.

The term Kegetation is related to plant life in an! area and is used for coerage of ground '! plants (hichare normall! aaila'le eer!(here in the 'iosphere. Kegetation term refers to the (ide area on thespatial scale coered '! plantation. The DKI is calculated from these indiidual measurements asfollo(s

&VI K (V&IR *VRE) B (V&IR OVRE)

9ere IR stand for near infrared and RED stands for red regions measurements of spectral reflectance.

*pectral reflectance ma! 'e defined as the ratio of reflected to incoming radiation respectie to spectral'and. The alue of DKI ma! 'e 'et(een the range starting from /5 to 5. "ecause of high reflectance inIR portion of the EM*, health! egetation is represented '! DKI alues 'et(een G.5 and 54F?7.

VII% S=D4 AREA

ocus of our stud! is on regions in the icinit! of Ramganga Rier, (hich 'elongs to the "areill! districtin India. "areill! is a large and prosperous cit! of Uttar Pradesh. It has a population of almost 5.F millionpeople. "areill! is a cit! in "areill! district in the northern Indian state of Uttar Pradesh.

District "areill! is locked in India at northern region and on F5G, LF6E position. "areill! has

Eastern 'oarders of *hah0ahanpur and Pili'hit, -estern 'oarder of Rampur, orthern 'oarder of Udham*ingh agar #U$ and *outhern 'oarder of "adaun. It is situated at the Ramganga rier3s 'ank and thisdistrict passes through seen riers. It is situated 5GGkm north of the lo(er 9imala!an range.


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igure ?. Multi *pectral Image from )I** ? M@ sensor of "areill! Region

VIII% RESD@= A&A@4SIS

To anal!2e of the multi spectral image, (e hae emplo!ed different Image Processing algorithms like+rtificial eural et(orks #+$, Discrete -aelet Transforms #D-T$, Discrete Cosine Transforms#DCT$ and DKI #ormali2ed Difference Kegetation IndeB$ to generate alse Color Composite related tomulti spectral image or to emphasi2e some of the features of the image that ma! 'e the particular regions

or piBels of the image.

#% Anal1sis of =he 7ulti Spectral Ima.e Dsin. A&&

*tep 5 /or +ssem'ling the Training Data (e hae receied the image of the "areill! Ramganga region assho(n in the figure ? and '! using the Data Cursor tool in the M+T)+" (e hae o'tained the R//"components of the piBels (hich 'est represent the different features of the image like the Rier < -ater"odies, the Concrete *tructures, the Roads and the Kegetation. Thus (e o'tained R//" alues of almost5GG piBels and these alues ma! 'e moulded to form a 6@5GG matriB.R 5FG 5F 565 56H 5?F 5>5 5H; 5LFQ.. G>; G?; G>5 G5 G>F 5G? G;H GL?Q.." G>H G>F GHG GLH G> G;H GL G;Q..MatriB of Input PiBelsR G.G G.G G.H G.H G.H G.G G.G Q.. G.L G.L G.L G.G G.G G.G G.GQ.." G.G G.G G. G. G G. G.G G.GQ..MatriB of Target CC

*tep F/ To Create the et(ork '0ect (e define the net(ork and specif! its features like no. of neurons,rangee of the alues of the input neurons, no. of la!ers etc. and specif! the input and target matrices. Intarget matriB, there is a particular color for the particular feature to generate the CC.*tep 6/*imulate the et(ork Response for -hole the Image, The o'tained function representing therelation 'et(een the input and the target, (e are read! to generate a resulting matriB corresponding tothe final CC of the gien image. "ut, 'efore (e simulate the image (ith the help of gien net(ork of

neurons, (e are to conert the 6/dimensional matriB of dimensions N>5F @ >5F @ 63 corresponding to themulti spectral image into a 6/dimensional matriB of dimensions N6 @ >5F@>5F3 i.e. N6 @ FHF5??3.o( thisconerted form of the multi spectral image applied to neural net(ork for the simulation.

igure >. CC Image of the Multi *pectral image

2% Anal1sis of the 7ulti Spectral Ima.e Dsin. H=


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*tep 5/ In the er! first step (e receie the multispectral image and conert it into the gra!scale imageas in figureLa to appl! the D-T algorithm on the image.

igure H. #a$ ra!scale Image #'$ +eraged Component

*tep F/ +fter (e hae got the gra!scale image, (e appl! the aeraging and differencing algorithm of thediscrete (aelet transform on the multi spectral image and anal!2e the aeraged component of thismultispectral image, as sho(n in the figure H'.

This +eraged component of the image is considered as the original image and the D-T algorithm is

again applied on this aeraged component. +mong the resulting components the aeraged component isagain anal!2ed. This process of calculating D-T is repeated four to fie times and each aeragedcomponent is anal!2ed for the presence of the piBels representing the corresponding features.

*tep 6/ o( as (e hae o'tained the fifth aeraged component of the multi spectral image, the inerseprocess is to 'e undergone. In this process of calculating inerse D-T, the four resulting components ofthe input image are considered to 'e the input of the inerse filter.

+fter (e appl! the four components aeraged, ertical, hori2ontal and diagonal, (e get the earlieraeraged component of the image. +nd after repeating this inerse filtering process (hat (e get as theresult is the original multi spectral image.

*tep ?/ o(, (e finali2e our stud! '! anal!2ing all the aeraged components and tr! to check out thefeatures of the multispectral image. -hat turned out to 'e our result of the D-T anal!sis is that sho(n inthe figure.

'% Anal1sis 5f =he 7ulti Spectral Ima.e Dsin. (C=)

*tep 5/ In the er! first step (e receie the multispectral image and conert it into the gra!scale image.*tep F/ +fter achieing gra! scale image, (e appl! the DCT algorithm on this gra!scale image and get theDCT coefficients of the multi spectral image, sho(n in the figure.

igureL. DCT Coefficients Image and Compressed matriB igure . Compressed Images

*tep 6/ o(, that (e hae o'tained the DCT coefficient matriB, (e can go for scaling these coefficients,and can get a smaller matriB as sho(n here under.

*tep ?/ If (e desire to get the original image 'ack from the coefficients, the inerse DCT algorithm (ill 'eemplo!ed. 9ere if the image containing all the coefficients is used then (hat (e get as result is theuncompressed all the same original gra!scale image. "ut if (e use the smaller coefficients matriB then (eget a compressed gra!scale image of the original image (hich is gien herein figure.


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The anal!sis of this compressed image sho(s that the piBels representing the egetations in the multispectral image are not as clear, 'ut the piBels representing the (ater 'odies and the concrete structureare 1uite reealing.% Anal1sis of =he 7ulti Spectral Ima.e Dsin. &VI

*tep 5/ Colour image receied '! us has the components kno(n as R" (here R stands for near Infrared'and, stands for Red 'and and " stands for reen 'and.*tep F/+fter getting the images of the different 'ands no( our aim is to find out the DKI alues of thegien image DKI S #IR RED$ = #IR RED$ "! using the M+T)+" (e got the DKI alues of eachpiBel and finall! (e got image as sho(n in the figure. The alues of DKI are in the range of /5 to 5.ormaking the image (e hae conerted this range to G to FGG as DKI image alue S #DKI alue 5$&5GG

igure ;. DKI image igure 5G. alse Color Composite of Multi *pectral Image

*tep 6/Construction of the false color composite$% Accurac1 and comparisons of results

9ere is a ta'le giing an approBimate anal!sis of all these four algorithms and there results

Algorithms Pixels of

water

bodies

Pixels of

concrete

str.

Pixels

of

roads

Pixels of

vegetation

ANN 80% 85% 75% 90%

DWT 70% 40% 75% 50%

DCT 70% 80% 40% 30%

NDVI 40% 70% 50% 88%

Ta'le 5. Results of Karious +lgorithms igure 55. Comparison chart

G% Results Calculated From A&& Resultant Ima.e

The area coered '! egetation is S #$+#+#%2 m2 The area coered '! rier and (ater 'odies is S 2G+2"%9' m2 The area coered '! roads is S G$+'%2G m2 The area coered '! concrete structures is S G"#2$G'%# m2

Total area of "areill! region under our stud!S 22292+%+2 m2 Ve.etation K G9%"P

Rier and (ater 'odies S #%#9P Roads and streets S '%"$P Concrete structures S 2G%"P

nce (e hae o'tained the egetation further the egetation land is to 'e classified to cultiated landused for farming and other no farming land. This farming land proides us one of the er! useful data formathematical model for optimi2ing !ield and for precision modeling.

IN% ARICD@=DRE ARC:I=EC=DRA@ 75E@

9ere (e are tr!ing to predict most faora'le com'ination of input for certain region of hectare of land.This result as a reference is useful to increase the productiit! of land. Though man! of the agriculturemodel (as gien '! man! researchers like/ )o(en'erg De Doer in 5;;; inoled in ealuation of thepotential of precision farming for production risk management related to crops of agronom!. Earth*can


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+rchitectural Model+t the heart of Earth*can is the Tierra *tation, a data (arehousing and distri'utionsoft(are suite deeloped '! Photon Research +ssociates, Inc. of *an Diego, California. *oil moisturemodel + model to compute soil moisture (as deeloped using the E*T+R micro(ae radiometer. -ei 89ang and *huheng :on s!stem for sensors and Michael Rashet Model etc.

igure5F. *!stem Model to determine agriculture !ield

#% 4ield 7onitorin. for precision Farmin.

*patial monitoring related to crop !ield is deemed initiali2ation for the crop management at a *ite leel. +Crop management s!stem haing precision alue ma! 'e deeloped (ith the com'ination of data of crop!ield (ith images, topographic map and soil map. or determination of !ield for indiidual field or (holefarms, a method kno(n as collect < (eight is 'een used '! the farmers for seeral !ears. "ut for lastsome !ears farmers started using !ield monitors (ith electronic s!stems (hich are more adancedmethods for !ield measurements. These !ield monitors not onl! monitors 'ut also records the !ield forharested crops. These !ield monitoring s!stems includes the sensors related to/ grain moisture, grainflo(, ground speed and displa! console and a s(itch (ith header position. Com'ination of the datareceied '! the !ield monitor (ith the each data point location ma! 'e used for producing the desired!ield map.

2% 7odelin. parameters

There are fifteen t!pes of input parameters are used in our agriculture s!stem model, (hich are ManPo(er, T!pe of )and, Total amount of fertili2er, Pesticides needed, *eed amount, Irrigation needed,Technolog!, Temperature, Relatie humidit! of land, *olar energ!, -ind, p9 of land, encing, Rainfall andAualit! of minerals."! ar!ing a'oe parameters at different alues (e achiee the optimi2ed !ield and different graphs can'e o'tained (ith respect to !ield s parameters.

'% raphical Anal1sis of 4ield For Various Input 3arameters

A% 7an 3o/er

raph 'et(een man po(er Ks !ield is gien 'elo(. rom this graph (e can anal!2e that man po(erreached at its peak (hen (e used ;Gunit of it and after that it decreases and then constant. *o if (e used

;G unit man po(ers then (e (ill get maBimum !ield.% @and

raph 'et(een land t!pe Ks !ield is gien 'elo(. "ecause of three t!pes of land is aaila'le#5$/ ertili2er land#F$/ Medium fertili2er land#6$/ )ess fertili2er land*o as graph sho(s if here (e used fertili2er land then (e (ill get maBimum !ieldC% Fertili6ers

raph 'et(een fertili2er Ks !ield is gien 'elo(. +s graph sho(s if (e used F>Gkg fertili2er per hectare,then (e (ill get maBimum !ield. +nd if (e used less or more fertili2er then (e cannot get such result% 3esticides

raph 'et(een man Pesticides Ks !ield is gien 'elo(. Pesticides are used to reduce the effect of diseaseproduce '! insects. If (e used Fliter of it then our !ield (ill (e maBimum.E% Seed


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raph 'et(een *eed amount Ks !ield is gien 'elo(. +s graph sho( after 5GG kg it is constant, mean thatif (e used seed more than 5GG kg, then that (ill us (astages 'ecause !ield (ill not increased. -e (ill getmaBimum !ield if (e used 5GGkg of seed.

F% Irri.ation

Irrigation or (ater suppl! is needed for the gro(th of plants as (ell as is necessar! for canop! leaf areadeelopment. Un'alanced alue of moisture #high or lo($ ma! results in leaf eBpansion or cellprogressie declination (hich ma! result in lo( leel of green leaf duration and leaf area (hich furtherresults as declination in !ield potential and accumulation of total dr! matter. raph 'et(een Irrigation Ks!ield is gien 'elo(. +s graph sho(s if (e FG unit of irrigation then (e (ill get maBimum !ield.% =echnolo.1

It is er! important parameter (hich t!pe of technolog! !ou are using. :ield maB. if used TSFG.:% =emperature

This is one of the important parameters. +de1uate temperature is necessar! for the proper gro(th ofcrops. :ield (ill 'e maBimum if temperature is FG degree Celsius.I% Relative :umidit1

This term ma! 'e defined as the ratio of alue of moisture in air to saturation capacit! of air at some

specific temperature. The alue of Relatie 9umidit! of ?G/HG found suita'le for most of the cropplants.J% Solar radiations

*olar radiation is an affecting factor for crops in post harest, harest or germination state. Radiations inisi'le range hae importance for plants photos!nthesis reaction. Radiations, (hich are actie forphotos!nthesis in the range from ?GGGV to LGGGW, are re1uired for crop production.;% Hind velocit1

-ind in moing condition is necessar! as it proides heat, moisture and fresh CF needed forphotos!nthesis reaction. The alue of -ind elocit! 'et(een ? to H km=hrs is re1uired for crops.@% p: Value

p9 alue of soil is also responsi'le for crop gro(th. eutral soils haing p9 alue e1ual to L.G are 'estsuita'le for crop gro(th. "ut soils haing lo(er alue are not suita'le for crop gro(th as the! are acidicand high toBic (ith +l and e.7% Fencin.

Proper fencing is re1uired in order to preent the crop from animals. If fencing is not proper then cropproduction (ill decrease.&% Rainfall

Rainfall is necessar! for 'etter 1ualit! of crop gro(th and production. Crop !ield is maBimum if rainfallis een.5% 7inerals

The 1uantit! of minerals is 1uite necessar! in the soil. Mineral content increases the productiit!.

y= -12mn+5375

Graph between Man Power Vs Yield

y= -100L+4802

Graph between Type of Land Vs Yieldy= -1.55fr+4647

Graph between Fertilizer Vs Yield


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y= -200p+4682

Graph between Pesticides Vs yieldy= -4s+4682

Graph between Seed amount Vs Yield

y= -12.60irr+4458

Graph between Irrigation Vs Yield

y= -24tech+4742

Graph between Technology Vs Yield

y= -16.58temp+4754

Graph between Temp. vs yield

y= -24rh+5498

Graph between rel. humidity vs yield

y= -1600rad+5143

Graphs between radiation vs yieldy= -200velo+5322

Graph of wind velocity vs yield

y= 25ph+4382

Graph between pH vs yield

y= -50f+4532

Graph between fencing vs yield

y= .977rain+1.022

Graph between rainfall vs yield

y= -50m+4532

Graph between minerals vs yield

igure 56. raphical +nal!sis of :ield or Karious Input Parameters5ptimi6ed .raph

The final graph that indicate the effect of all the parameters on the yield is shown below.


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igure 5?. raph 'et(een modeling parameters s !ield+dding a'oe e1uations and soling for the optimi2e e1uation4K!"%a!G%G+!"%#"'c!#'%''d!"%2G+e!"%f!#%G.!#%#"$'h!#%Gi!#"G%G+0!#'%''O#%G+l!'%''mO"%"G+n!%''oO$"'%G

The a'oe e1uation is the approB. optimi2ed e1uation.

-here aS Man po(er 'S)and cSertili2ers dSPesticides eS*eeds fSIrrigation gSTechnolog!hSTemperature iSRelatie humidit! 0S*olar radiation kS-ind elocit! lSp9 mSencing nSRainfalloSMineralso(, su'stituting the optimi2ed alue of all the modeling parameters-e hae. 4K "G"%9

This is the maBimum !ield o'tained from graphs. "ut the alue of maBimum !ield o'tained from optimi2ed alue is 4K2 9ence the percentage error KQ("G"%9!2)B2H piBels +rea coered '! one piBel S FFGG s1uare meter *o Total egetation area S 5G;>H&FFGG S F6;LG6FGG s1uare meter S F6;LG.6F 9ectare MaBimum :ield per 9ectare S ??Fg or ?? Auintal *o Total !ield S F6;LG.6F&?? S 5G>?H;?.GAuintal S 5G>?H;?G. Ton

+s (e can see, an! nation=region ma! 'e modeled related to production of an! crop using these methods(hich ma! 'e helpful for decision makers or researchers for making an understanding of the eBtent andstatus related to field < crop management, soils and climate. Method used for the anal!sis of theproductiit! of )and has a lot of adantage to improe the productiit! of )and of certain region '!adopting certain com'ination of Inputs and suita'le Technolog! for that )and. The "areill! regions haesand! soil all around in the region.

N% C5&C@DSI5& A& FD=DRE SC53E

In this Research (ork (e demonstrate Remote *ensing a'ilit! related to capturing the spatial datathrough remote sensing. If (e eBamine the results o'tained from the four algorithms applied on themultispectral image, it is found that there are different piBels o'tained '! different algorithms. The +method has all the er! good results for all the four features presented here in the multispectral imageand almost all the piBels are trained in compare to DCT, D-T and DKI. -ith the use of h!per spectralimages < much higher resolution (ith the use of satellites all a'oe errors of classification ma! 'eomitted, hence goernment of India (ill hae to launch the satellites haing such capa'ilities in coming

times.

+'oe mentioned calculation of crop !ield prediction model is for "areill! Region it ma! slightl! ar! forother region. The calculated !ield and actual !ield ma! differ significantl! 'ecause this model is 'asedupon normal climate condition.

The crop !ield ma! 'e forecasted (ith the use of the model deeloped in this paper as this paperrepresents promising results.

Crop !ield prediction ma! ar! due to seeral other local regions like/ human actiit!, diseases or pets.This ma! limit seriousl! in methods related to forecasting for an! denomination. "ut use of + in ourmodel reduces the chance of affecting forecast due to pets or diseases, (hich affect the egetationdirectl!. These methods of prediction related to the !ield of crops are eBpected for 'est result ofprediction as the! hae lo(er alue of residual (ith the comparison to the data of histor!.


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The data proiders from Remote *ensing are isuali2ing the agriculture industr! as largest market. Thesensors used in Remote *ensing receie data from reflected energ! from soil or plant surface. Thetechnolog! of Remote *ensing uses the ph!sics of er! complicated leel. 9ere, operators of farming'ecome dependent upon precision farming consultants and professional engineers for processing ofremote sensed data #in ra( form$ into information of use for making decisions related to farmingmanagement. The Remote *ensing technolog! has a'undance for measuring the aria'ilit! in soil andplants. -e hae also the lace of information related to the aria'ilit! of condition of plants and thisaria'ilit! is to 'e managed for improing the production of crops. The restriction in the deelopment ofmanagement decision support s!stems for precision farming is nothing 'ut the lack of kno(ledge.Precision farming includes Remote *ensing as one component onl!. Indian agriculture industr! neededthe deelopment of Precision arming as profita'le and practical management tool.NI% References

[1] C.Toomey, William marks, Satellite Image Dissemination via software agents IEEE Trans.,Vol 10, no. 5 ,PP (44-

51), Oct 2005.

[2] A.K Charkaborti, V.V.Rao, Performance evaluation of an irrigation project using satellite remopte sensing GIS &

GPS. Water resources group. National Remote sensing agency, Hyderabad. Feb 2005.

[3] C.Unsalan , K.L. Boyer , A Theoratical and Experimental Investigation of graph theoretical Measures for lands

development in Satellite Imagery. IEEE Trans Pattern analysis & machine intelligence, vol.27, no.4 PP (575-589), April

2005.

[4]V.T.Diverio , A.T Gomez , F.S.Osorio, A neural approach to classification of satellite images. IEEE Trans XV

Brazilian symposium on computer graphics and image processing. Feb 2002

[5]Lin Zhou, C. Kambhamettu, D.B. Goldgof , Extracting Non-Rigid Motion & 3-D structure of Hurricanes from satellite

Image sequence without correspondance. IEEE Trans. Computer vision and Pattern Recognition Vol.2 PP (2280). June

1999

[6] C.B.Medeiros, G.Vossen, M.Weske , GEO-WASA-combining GIS technology with work flow management . IEEE

trans. 7th Israeli Conference on Computer Based Systems and Software Engineering. PP 129, JUNE 1996

[7] A.Jalobeanu, L.Blanc-Feraud , J.Zerubia , Estimation of Adaptive Parameter for Satellite Image Deconvolution.

IEEE Trans. International Conference on Pattern Recognition. Vol-3, PP (3322), Sept 2000.[8] J.Cohen, I.Herlin, A motor Computation and interpretation framework for oceanographic and interpretation

framework for oceanographic satellite images. IEEE Trans. International Symposium on Computer Vision. P-13. Nov

1995.

[9] M.Recce , J.Taglor , A.Plebe , G.Tropoiano, Vision and Neural Control for an orange harvesting Robot. IEEE Trans.

1996 International Conference Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image

Processing. P 467, Aug 1996.[10] D.Geman , Bruno.J, An Active Testing Model for tracking Road in Satellite Images. IEEE trans. of Pattern Analysis

and Machine Intelligence. Vol.18 No.1 PP (1-14), Jan 1996.

[11] O.Badini, L.Dioni, Application of Rain fall Analysis, Biophysical modeling and GIS to AgroClimate Decision

support in Madiama Commune. CRSP Research Scientific Synthesis Conference, Nov 2001.

[12] V.M.mayande, I.Srinivas, Decision Support System for Selection of Farm Machinery in Dryland Agriculture Based

on Timeliness and Precision. Central Research Institute for DryLand Agriculture. Hyderabad 2004.[13] Gonzalez, Woods, Eddins, Digital Image Processing. 2nd edition, Prentice Hall.

[14] RICHARDS, A.J. and WIEGAND, C.L., 1977, Distinguishing vegetation from soil background information.

Photogrammetric Engineering & Remote Sensing, 43, pp. 15411552.[15] TUCKER, C.J., 1979, Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing

of Environment, 8, pp. 127150.

[16] Arvind C. Pandy and M. S. Nathawat 2006. Land Use Land Cover Mapping Through Digital Image Processing ofSatellite Data A case study from Panchkula, Ambala and Yamunanagar Districts, Haryana State, India.

[17] Coppin, P. & Bauer, M. 1996. Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote

Sensing Reviews. Vol. 13. p. 207-234.

[18] Dimyati, et al.(1995). An Analysis of Land Use/Land Cover Change Using the Combination of MSS Landsat and

Land Use Map- A case study of Yogyakarta, Indonesia, International Journal of Remote Sensing 17(5): 931 944.[19] Macleod & Congalton. 1998. A Quantitative Comparison of Change Detection Algorithms for Monitoring Eelgrass

from Remotely Sensed Data. Photogrammetric Engineering & Remote Sensing. Vol. 64. No. 3. p. 207 - 216.

[20] Ahmed, N., T. Natarajan, and K. R. Rao. 1974. On image processing and a discrete cosine transform. IEEE

Transactions on Computers C-23(1): 90-93.

[21] Riebsame, W.E., Meyer, W.B., and Turner, B.L. II. 1994. Modeling Land-use and Cover as Part of GlobalEnvironmental Change. Climate Change. Vol. 28. p. 45.[22] Shoshany, M, et al (1994). Monitoring Temporal Vegetation Cover Changes in Mediterranean and Arid Ecosystems

Using a Remote Sensing Technique: case study of the Judean Mountain and the Judean Desert. Journal of Arid

Environments, 33: 9 21.


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G - 2"#$, IJAFRC All Ri.hts Reserved ///%i0afrc%or.

[23] Singh, A. 1989. Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote

Sensing. Vol. 10, No. 6, p. 989-1003.

[24] Benedetti, R., Rossinni, P., 1993. On the use of NDVI profiles as a tool for agricultural statistics: the case study of

wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ. 45, 311326.

[25] Garcia-Paredes, J.D., Olson, K.R., Lang, J.M., 2000. Predicting Corn and Soybean productivity for Illinois soils.Agric. Syst. 64 (3), 151170.

[26] Goward, S.N., Markham, B., Dye, D.G., Dulaney, W., Yang, J.,1991. Normalized difference vegetation indexmeasurements from the advanced very high resolution radiometer. Remote Sens. Environ. 35, 257277.

[27] Kogan, F.N., Gitelson, A., Zakarin, E., Spivak, L., Lebed, L., 2003.AVHRR-based spectral vegetation index for

quantitative assessment of vegetation state and productivity: calibration and validation. Photogrammetric Eng. Remote

Sens. 69 (8), 899906.

[28] Hand book of IRS P-6 Resouresat-1 and its sensors.

analysis of remote sensed data using different image processing algorithms applied to agriculture...

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