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Evaluating the Yield of Hybrid Napier Grass with Data Mining Techniques

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 5
Year of Publication: 2011
Authors:
Nadiammai G. V.
Krishnaveni S.
M. Hemalatha
10.5120/4394-6100

Nadiammai G V., Krishnaveni S. and M Hemalatha. Article: Evaluating the Yield of Hybrid Napier Grass with Data Mining Techniques. International Journal of Computer Applications 35(5):1-7, December 2011. Full text available. BibTeX

@article{key:article,
	author = {Nadiammai G. V. and Krishnaveni S. and M. Hemalatha},
	title = {Article: Evaluating the Yield of Hybrid Napier Grass with Data Mining Techniques},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {35},
	number = {5},
	pages = {1-7},
	month = {December},
	note = {Full text available}
}

Abstract

Data Mining is the process of identifying the hidden patterns from large amount of data. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning techniques are mainly focused as research through which we learnt to recognize complex and make intelligent decisions based on data. This paper involves the information about the yield of the hybrid grass from NBH1 to NBH11. The hybrid grass enhances the milk production in the states of Tamilnadu, Kerala, Karnataka, Andhra Pradesh, Orissa, and Maharashtra & Gujarat. It is well adapted to the soil and climatic conditions of Tamilnadu. In this paper, some of classification models are used to predict the yield of hybrid grass. They are NaiveBayes, J48, Rule Induction, Single Rule Induction, Decision Stump, ID3 and Random Forest.

References

  • Aken’ova M.E. and H.R.Chheda. 1981. Morphology, cytology and forage potential of Pennisetum americanum (1) K.Schum.x P.Purpureum schum. Amphidiploids Euphytica 30, pp. 397-404. Publishers, Dorterecht, Netherlands. Pp. 343
  • Babu,C., Sundramoorthi,J., Vijayakumar.G and Ganesh Ram.S, 2009. Analysis of Genetic Diversity in Napier Grass (Pennisetum purpureum Schum) as detected by RAPD and ISSR Markers. J.Plant Biochemistry & Biotechnology Vol 18(2), 181-187.
  • Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 532. doi:10.1023/ A:1010933404324
  • DAHUS-Director of Animal Husbandry and Veterinary Services Report, 2004, Chennai, Tamil Nadu.
  • Data Mining and Data Warehousing available at:http://databases.about.com/od/datamining/g/gclassification.htm
  • Domingo’s, Pedro & Michael Pazzani (1997) “On the optimality of the simple Bayesian classifier under zero-one-loss”. Machine Learning, 29:103-137.
  • Gildenhuys, P.J.1950. A new fodder grass, Farming in South Africa, 15, 189-191.
  • Hanna,W.W., C.J.Chaprro, B.W.Mathews, J.C.Burns, L.E.Sollenberger, and J.R.Carpenter. 2004. Pernnial Pennisetums. Pp.503-535. In L.E.Moser, B.L.Burson, and L.E.Sollenberger (Ed.). Warm season (C4) grasses. ASA/CSSA/SSSA,Madison,WI.
  • Hedge, N.G., 2007. Transfer of technology for forage production, BAIF Development Research Foundation, Pune (Personal Communication)
  • http://en.wikipedia.org/wiki/Naive_Bayes_classifier
  • http://en.wikipedia.org/wiki/Rule_induction
  • http://dms.irb.hr/tutorial/tut_dtrees.php-id3
  • Krishnaswamy.N. and V.S.Raman, 1949. A note on the Chromosome numbers of some economic plants of India.Curr.Sci., 18.376-375.
  • Loper, Edward L.; Bird, Steven; Klein, Ewan (2009). Natural language processing with Python. Sebastopol, CA: O'Reilly. ISBN 0-596-51649-5.
  • Orodho.A.B. 2006. The role and importance of Napier grass in the smallholder dairy industry in Kenya, P.O.box 1667, kitale-30200, Kenya.FAO edition. Http://www.fao.org/ag/AGP/AGPC/doc/Newpub/napier/napier_kenya.html.
  • Osgood, R.V.Hanna.W.W, Chaparro and B.W.Mathews.1997.Hybrid seed Production of Pearl millet x Napier grass triploid hybrids. Crop Science 37(3):998-999. {a}USDA-ARS, Coastal Plain Exp.Stn, Tifton, GA, USA.
  • Pritchard, A.J (1971).The hybrid between Pennisetum typhoid’s and P.Purpureum as a potential forage crop in South Eastern Queensland. Tropical Grasslands, Vol.5, No.1, PP.35-39.
  • Quninlan, J.R.C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
  • Wayne Iba and Pat Langley, (1992). Induction of One-Level Decision Trees. Proceedings of the Ninth International Conference on Machine Learning.