International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.6, No.10, Sep. 2014

Rough Set Model for Nutrition Management in Site Specific Rice Growing Areas

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K. Lavanya, N.Ch.S.N. Iyengar, M.A. Saleem Durai, T. Raguchander

Index Terms

Rough Set, Attribute Reduction, NPK Supplies


The optimized fertilizer usage for better yield of rice cultivation is influenced by key factors like soil fertility, crop variety, duration, season, nutrient content of the fertilizer, time of application etc., It is observed that 60 percent of yield gap in tamilnadu is due to farmers lack of knowledge on key factors and informal sources of information by pesticide dealers. In this study the major contributing factors for fertilizer requirement and optimum crop yield were analyzed based on rough set theory. In data analytics perspective the nutrient plan is sort of multiple attribute decision-making processes. To reduce the complexity of decision making, key factors that are indiscernible to conclusion are eliminated. Our rough set based approach improved the quality of agricultural data through removal of missing and redundant attributes. After pretreatment the data formed as target information, then attribute reduction algorithm was used to derive rules. The generated rules were used to structure the nutrition management decision-making. The precision was above 88% and experiments proved the feasibility of the developed decision support system for nutrient management.

Cite This Paper

K. Lavanya, N.Ch.S.N. Iyengar, M.A. Saleem Durai, T. Raguchander,"Rough Set Model for Nutrition Management in Site Specific Rice Growing Areas", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.10, pp.77-86, 2014. DOI: 10.5815/ijisa.2014.10.10


[1]Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data”, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991 .

[2]Dr. K.V. Rao , “Site–Specific Integrated Nutrient Management for Sustainable Rice Production and Growth”, from internet,2012,

[3]M. Magnani, “Technical Report on Rough Set Theory for Knowledge Discovery in Data Bases”, from internet, 2003.

[4]P.S. Swathi Lekshmi,K Chandrakandan and N Balasubramani, “Yield Gap Analysis among Rice Growers in North Eastern Zone of Tamil Nadu”,Agricultural Situation in India, vol. 63 no. 2,pp 729-773, 2006,

[5]Z.Pawlak , “Rough Sets”, International Journal of Computer and Information Sciences, vol 11, no. 5, pp 341–356, 1982.

[6]T. Y. Lin, Y. Y. Yao and L. A. Zadeh , “Data Mining, Rough Sets and Granular Computing”, Physica– Verlag, pp.102-124, 2002.

[7]Z. Pawlak and A.Skowron, “Rudiments of rough sets”, Information Sciences, Elsevier,vol.177 ,no. 1,pp 3–27, 2007.

[8]Qiang Shen and Alexios Chouchoulas, “Rough Set – Based Dimensionality Reduction for Supervised and Unsupervised Learning”, International Journal of Applied Mathematics and Computer Sciences, vol. 11, no. 3, pp 583-601, 2001.

[9]Jiye Li and Nick Cercone, “Discovering and Ranking Important Rules” ,KDM Workshop, 2006, Waterloo, Canada, Oct 30-31,pp.152-171

[10]S Joseph, PP Ouseph, “Assessment of nutrients using multivariate statistical techniques in estuarine systems and its management implications: a case study from Cochin Estuary, India”, Water and Environment Journal,vol 24, no. 2, pp 126-132, 2010.

[11]N.Xiong, and L.Litz, “ Reduction of fuzzy control rules by means of premise learning - method and case study” , Fuzzy Sets and Systems, vol.132,no.2,pp217-231.2002.

[12]J.Han , X.Hu and T.Y. Lin, “ Feature Subset Selection Based on RelativeDependency between Attributes” , Rough Sets and Current Trends in Computing: 4th International Conference, RSCTC Uppsala,Sweden,June1-5,pp.176–185,2004.

[13]A.Kangaiammal, R.Silambannan, C.Senthamarai,M.V. Srinath, “Student Learning Ability Assessment using Rough Set and Data Mining Approaches”, IJMECS vol. 5, no. 5, June 2013

[14]Ali Khazaee, “Heart Beat Classification Using Particle Swarm Optimization”, IJISA,vol. 5, no. 6, May 2013

[15]CN. Mac Parthaláin,., R.Jensen and Q.Shen, “ Fuzzy entropy-assisted fuzzy rough feature selection”, Proceedings of the 15th International Conference on FuzzySystems,pp423-430,2006

[16]M.G. Smith and L.Bull , “Feature construction and selection using genetic programming and a genetic algorithm”, Proceedings of the 6th European Conference on Genetic Programming , EuroGP Essex,UK,April,vol 14-16,pp229–237,2003

[17]X.Y. Wang, J.Yang, X.Teng , W.Xia, Wand R.Jensen,“Feature Selection based on Rough Sets and ParticleSwarmOptimization”,PatternRecognitionLetters,vol. 28,no. 4,pp 459–471, 2004,

[18]P. Mitra, C.A.Murthy and S.K Pal, “Unsupervised feature selection using feature similarity”, IEEE Transactions on Pattern Analysis and Machine Intelligence,Washington, DC, USA ,vol 24,pp 301-324, 2004,

[19]Liu , Huan and Hiroshi Motoda, “Computational methods of feature selection”. Chapman and Hall/CRC,2007.

[20]K.Zuhtuogullari , N.Allahverdi and N.Arikan, “Genetic algorithm and rough sets based hybrid approach for reduction of the input attributes in medical systems”,International Journal of Innovative Computing, Information and control,vol.9,no.7,pp 3015-3037,2013

[21]X.Pan and S. Zhang, “ Remote sensing image feature selection based on á-torrent rough set theory” , 7th International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, Shandong,China,pp.1034-1038,August2010

[22]C.C Hsiao, , Yi-Wei Ku , “ A predictor from numerical data based on fuzzy sets and rough sets”, 3rd International Workshop on Advanced Computational Intelligence, IWACI,Suzhou, China, August 25-27 ,pp.139-144,2010.

[23]N.Ishii , Y.Morioka, H.Kimura,Y.Bao, “Classification by multiple reducts-KNN with confidence”, 11th International Conference on Intelligent Data Engineering and Automated Learning,Paisley,UnitedKingdom,,vol 6283,pp94-101, September1-3,2010

[24]N.M. Parthaláin, R.Jensen , Q.Shen, “ Finding fuzzy-rough reducts with fuzzy entropy” , IEEE international conference on fuzzy systems, Hong Kong, China, pp.1282-1288, June 1-6 2008

[25]A.Abraham, H.Liu, “Swarm Intelligence based rough set reduction scheme for support vector machines”, IEEE International Conference on Intelligence and Security Informatics,Taipei, Taiwan, pp. 200-202, June 17-20 2008.