Fuzzy C-Means Classifier for Soil Data

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1 International Journal of Computer Appliations ( ) Volume 6 No.4, September Fuzzy C-Means Classifier for Soil Data P.Bhargavi,M.S.,M.Teh Assoiate Professor Department of CSE Madanapalle Institute of Tehnology and Siene, Madanapalle Dr.S.Jyothi, M.S.,M.S.,PhD. Assoiate Professor & Head, Department of Computer Siene Sri Padmavathi Mahila Visva Vidyalayam, Tirupati. (Womens University) ABSTRACT: The distribution of soil lasses is an important fator in agriultural soils. In order to generate the soil lassifiation, fuzzy soil lassifiations were developed to provide the means to haraterize and quantify the soil lasses. This paper presents an index of fuzzy soil lassifiation generated by Fuzzy C-means lassifiation. The ability of lassifiation of the soils is tested with a Soil database. Fuzzy -means approah is also apable of handling the unertainty existing in soil parameters. As a result, fuzzy -means lustering an be suessfully applied to lassify soils. Key words: Soil Classifiation, Fuzzy C-Means, and Soil database.. INTRODUCTION Soil harateristis are single parameters whih are observable or measurable in the field or laboratory, or an be analyzed using mirosope tehniques. They inlude suh harateristis as olor, texture and struture of the soil, features of biologial ativity, arrangement of voids and pedogeni onentrations as well as analytial determinations. Soil properties are ombinations of soil harateristis whih are known to our in soils and whih are onsidered to be indiative of present or past soil-forming proesses. Soil horizons are three-dimensional pedologial bodies whih are more or less parallel to the earth's surfae. Eah horizon ontains one or more property, ourring over a ertain depth, whih haraterizes it. Soils are defined by the vertial ombination of horizons, ourring within a defined depth, and by the lateral organization of the soil horizons, or by the lak of them, at a sale refleting the relief or a land unit. Soil texture is an important property for agriultural soil. Soil texture is a permanent feature of a soil, unless subjeted to erosional depositions or removals. It influenes fertility, drainage, water holding apaity, aeration, tillage, and strength of soils. In this paper we are applying Fuzzy C-means lassifier to lassify soil texture based on the properties of soils. Results of their study indiated that, Fuzzy C-means algorithm is apable of aurate real time soil profile lassifiation. This produes lear membership patterns.. FUZZ C-MEANS ALGORITHM The Fuzzy C-means (FCM) lassifiation algorithm is proposed by Bezdek [] aims to find fuzzy partitioning of a given training set, by minimizing the basi -means objetive funtional: where: N J(Z;U,V)= ( ik) m Z k - V i i= k= A U=[ ik] M f is a fuzzy partition matrix of Z V=[v, v.... v ], v i R n is a vetor of luster prototypes, to be determined Z k - V i is dissimilarity measure between the sample Z k and the enter V i of the speifi luster i (Eulidean distane) m (, ) is a parameter, that determines the fuzziness of the resulting lusters The minimization of J(Z;U,V), under the onstraint ( ik) = leads to the iteration of the following i= steps. k ( ik) m Z k (l) i= V i =, i To lassify unertainty data, Fuzzy C-means lassifier is very muh useful. Sine Soil data may have unertainty values to lassify soils, Fuzzy C-means lassifier is very muh useful. N ( ik) m k=

2 International Journal of Computer Appliations ( ) Volume 6 No.4, September and ik= { If D ik > and ik <,>, ( ik) = i= (D ik /Dj k ) /(m-) j= ik= The iteration stops when the differene between the fuzzy partition matries in two following iterations is lower than å. 3. SOIL CLASSIFICATION Soil lassifiation deals with the systemati ategorization of soils based on distinguishing harateristis as well as riteria that ditate hoies in use. Soil lassifiation is a dynami subjet, from the struture of the system itself, to the definitions of lasses, and finally in the appliation in the field. Soil lassifiation an be approahed from the perspetive of soil as a material and soil as a resoure. Engineers, typially Geotehnial engineers, lassify soils aording to their engineering properties as they relate to use for foundation support or building material. Modern engineering lassifiation systems are designed to allow an easy transition from field observations to basi preditions of soil engineering properties and behaviors. The most ommon engineering lassifiation system for soils is the Unified Soil Classifiation System (USCS). The USCS has three major lassifiation groups: () oarse-grained soils (e.g. sands and gravels); () fine-grained soils (e.g. silts and lays); and (3) highly organi soils (referred to as "peat"). The USCS further subdivides the three major soil lasses for larifiation. A full geotehnial engineering soil desription will also inlude other properties of the soil inluding olor, in-situ moisture ontent, in-situ strength, and somewhat more detail about the material properties of the soil that is provided by the USCS ode. For soil resoures, experiene has shown that a natural system approah to lassifiation, i.e. grouping soils by their intrinsi property (soil morphology), behavior, or genesis, results in lasses that an be interpreted for many diverse uses. Differing onepts of pedogenesis, and differenes in the signifiane of morphologial features to various land uses an affet the lassifiation approah. Despite these differenes, in a well-onstruted system, lassifiation riteria group similar onepts so that interpretations do not vary widely. This is in ontrast to a tehnial system approah to soil lassifiation, where soils are grouped aording to their fitness for a speifi use and their edaphi harateristis. Natural system approahes to soil lassifiation, suh as the Frenh Soil Referene System (Referential pedologique français) are based on presumed soil genesis. Systems have developed, suh as USDA soil taxonomy and the World Referene Base for Soil Resoures, whih use taxonomi riteria involving soil morphology and laboratory tests to inform and refine hierarhial lasses. Another approah is numerial lassifiation, also alled, ordination, where soil individuals are grouped by multivariate statistial methods suh as luster analysis. This produes natural groupings without requiring any inferene about soil genesis. In soil survey, as pratied in the United States, soil lassifiation usually means riteria based on soil morphology in addition to harateristis developed during soil formation. Criteria are designed to guide hoies in land use and soil management. As indiated, this is a hierarhial system that is a hybrid of both natural and objetive riteria. USDA soil taxonomy [9] provides the ore riteria for differentiating soil map units. This is a substantial revision of the 938 USDA soil taxonomy whih was a stritly natural system. Soil taxonomy based soil map units are additionally sorted into lasses based on tehnial lassifiation systems. Land apability Classes, hydri soil, and prime farmland are some examples. In addition to sientifi soil lassifiation systems, there are also vernaular soil lassifiation systems. Folk taxonomies have been used for millennia, while sientifially based systems are relatively reent developments [6]. A set of soil properties are diagnosti for differentiation of pedons. The differentiating haraters are the soil properties that an be observed in the field or measured in the laboratory or an be inferred in the field. Some diagnosti soil horizons, both surfae and sub- surfaes, soil moisture regimes, soil temperature regimes and physial, physio-hemial and hemial properties of soils determined were used as riteria for lassifying soils. The soils were lassified into different orders, sub-orders, great groups, sub-groups, families and finally into series as per USDA Soil Taxonomy[8]. 3. Soil Data In this paper Soil data onsists of attributes like (i.e., Depth, Sand, Silt, Clay, Sandbysilt, Sandbylay, Sandbysiltlay, TextureClass). The texture of the Soil data is varied from sand to silty lay loam where as in sub-surfae horizons it varied from sand to lay[]. Table shows the different soil survey symbols. Table : Soil Survey Symbols S Sil Si C Sand Silty Clay Loam Silty Clay Clay

3 International Journal of Computer Appliations ( ) Volume 6 No.4, September Sl Cl Sil L Ls Sl S Sandy loam Clay loam Silty Loam Loam Loamy sand Sand Clay Loam Sand Clay 3. Unertainty in Soil data It is well known that soil quality evaluation has not been fully quantified, as evidened by the ongoing debate in sientifi literature. The unertainty that is inherent in any evaluation proess involves both data and model ambiguity; this ambiguity inludes measurement error, inherent soil variability, soil instability, oneptual ambiguity, overabstration, simple ignorane of key fators that an impat soil quality. Beause of the wide range of fators that make up soil quality and its inherent unertainty, we believe that a unique approah must be taken to address soil quality. We propose that randomness and unertainty of soil quality be dealt with by using fuzzy sets theory and fuzzy logi [9]. This theoretial approah provides the basis for analysis of systems haraterized by a high degree of unertainty, nonlinearly and omplexity. In this paper the properties of attributes in the Soil database are speified with the speified quality standards. Quality has the property of fuzziness. If same type of texture ours with different omposition of attributes of Soil data, ambiguity may emerge. All the unertainty, in auray and ambiguity will bring fuzziness to the distribution of agriultural soil. The Soil data has Fuzziness-based unertainty. 4. EPERIMENTAL RESULTS AND DISCUSSIONS In this paper, we have applied fuzzy C-means algorithm for Soil data whih onsists of texture lasses. The fuzzy lassifiers lassify eah texture lass by lustering them. The lusters formed for the Soil data are shown from figure to figure... Figure : Cluster formed for soil texture silty lay loam.. Figure 3: Cluster formed for soil texture silty lay..... Figure : Cluster formed for soil texture sand... Figure 4: Cluster formed for soil texture lay 3

4 International Journal of Computer Appliations ( ) Volume 6 No.4, September... Figure : Cluster formed for soil texture sandy loam.. Figure 8: Cluster formed for soil texture loam.... Figure 6: Cluster formed for soil texture lay loam.. Figure 9; Cluster formed for soil texture sand lay loam... Figure 7: Cluster formed for soil texture silty loam... Figure. Cluster formed for soil texture sand lay.

5 International Journal of Computer Appliations ( ) Volume 6 No.4, September Eah row in the Soil dataset is a sample data point with lusters greater than one. The fuzzy lustering algorithm outputs a matrix of final luster enters where eah row provides the enter oordinates shown in figure and final fuzzy partition matrix and values of objetive funtion for eah iteration. The lustering proess stops when the objetive funtion improvement between two onseutive iterations is less than the minimum amount of improvement speified, with the auray off Figure : Final luster enters where eah row provides the enter oordinates.. CONCLUSION In this paper, we have applied C-means fuzzy lassifier for soil data, whih has several advantages suh as simple and systemati struture. In the luster generation proess, a lustering algorithm based on C-means is applied to luster the soil data suh that the training data an be lassified by the fuzzy lassifier. In addition it has good generalization ability and an ahieve lassified rate in the lassifiation problem of agriulture soil data. 6. REFERENCES [] R. Kruse, J. Gebhardt, and F. Klawonn, Foundations of Fuzzy Systems, Wiley, 994. [] Bezdek, JC (98). Pattern Reognition with Fuzzy Objetive Funtion Algorithms, Plenum, New ork. [3] Zimmerman, HJ (996). Fuzzy Sets Theory and Its Appliations, 3rd ed. Kluwer Aademi Publishing, Boston. [4] S. Abe, and R. Thawonmas, A Fuzzy Classifier with Ellipsoidal Regions, IEEE Trans. Fuzzy Systems, vo., no. 3, pp , 997. [] Jain, A.K, Murty, M.N, Flynn, P.J.: Data Clustering: A Review [6] Bonner, R.E.: On Some Clustering Tehniques. IBM, 964. [7] Ball, G.H., Hall, D.J.: ISODATA, A Novel Method of Data Analysis and Pattern Clasifiation. Standford Res. Institute, Menlo Park, [8] Fromm, F.R., Northouse, R.A.: CLASS, A Nonparametri Clustering Algorithm. Pattern Reognition. [9] Raja, A.,Meister, A., Martverk., P.: Fuzzy Classifiation Algorithms with Some Appliations. [] Baraldi, A., Blonda. P.: A Survey of Fuzzy Clustering Algorithms for Pattern Reognition. ICSI, TR-98-38, 998. Ching-Chang Wong, Chia-Chong Chen, and Shih-Liang eh: K-Means-Based Fuzzy Classifier Design. [] Setnes, M., Kaymak, U.: Extended Fuzzy Clustering Algoritms. [] MBratney, A.B., De Gruijter, J.J.: A Continuum approah to soil lassifiation by modified fuzzy k-means with extragrades. [3] A Thesis by D.Basavaraju Charaterisation and lassifiation of soils in Chandragiri mandal of Chittoor distrit,andhra Pradesh. [4] A thesis titled Charaterization, Classifiation and Evaluation of Soil Resoures in Sivagiri Miro-Water Shed of Pihatur Mandal, Chittoor Distrit, Andhra Pradesh. [] A thesis titled Morphology and Taxonomy of Soils in Ramahandrapuram Mandal of Chittoor Distrit, Andhra Pradesh. [6] George J. Klir and Bo an. Fuzzy Sets and Fuzzy Logi. Theory and Appliations, PHI. [7] Soil Survey Staff 998, Keys to soil taxonomy. Eight Edition, Natural Resoure Conservation Servies, USDA, Blaksburg, Virginia. [8] Soil Survey Staff 9, Soil Survey Manual. US Department of Agriultural Hand book No. 8. [9] Jager R., 99. Fuzzy Logi in Control. Delft TU Press, Delft, Belgium. Bhargavi Peyakunta is working as Assoiate Professor in the Department of Computer Siene and Engineering, Madanapalle Institute of Tehnology and Siene, Madanapalle, Andhra Pradesh. Eduational Qualifiations: M.S in Computer Siene from Sri Krishnadevaraya University, Anantapur and M.Teh degree from Sri Vinayaka missions University, Salem, India. Teahing & Researh Experiene: years of teahing experiene & years of researh experiene. Current Researh Interests: Data Mining, Fuzzy Systems, Geneti Algorithms and GIS. Jyothi Singaraju is working as Assoiate Professor & Head in the Department of Computer Siene, Sri Padmavathi Mahila Visvavidyalayam(SPMVV), Tirupati. Eduational Qualifiations: M.S in Applied Mathematis from S.V.University, Tirupati, M.S in Software Systems from BITS, Pilani, & Ph.D in Theoritial Computer Siene from S.V.University, Tirupati. Teahing & Researh Experiene: 6 years teahing experiene & 4 years researh experiene. Current Researh Interests: Fuzzy Systems, Neural Networks, Data Mining, Data Base Management Systems, Geneti Algorithms, Bioinformatis and GIS.

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