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Soybean Productivity Modelling using Decision Tree Algorithms

by S. Veenadhari, Dr. Bharat Mishra, Dr.CD Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 7
Year of Publication: 2011
Authors: S. Veenadhari, Dr. Bharat Mishra, Dr.CD Singh
10.5120/3314-4549

S. Veenadhari, Dr. Bharat Mishra, Dr.CD Singh . Soybean Productivity Modelling using Decision Tree Algorithms. International Journal of Computer Applications. 27, 7 ( August 2011), 11-15. DOI=10.5120/3314-4549

@article{ 10.5120/3314-4549,
author = { S. Veenadhari, Dr. Bharat Mishra, Dr.CD Singh },
title = { Soybean Productivity Modelling using Decision Tree Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 7 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number7/3314-4549/ },
doi = { 10.5120/3314-4549 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:13:08.967859+05:30
%A S. Veenadhari
%A Dr. Bharat Mishra
%A Dr.CD Singh
%T Soybean Productivity Modelling using Decision Tree Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 7
%P 11-15
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining applications in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. In the present study an attempt has been made to study the influence of climatic parameters on soybean productivity using decision tree induction technique. The findings of Decision tree were framed into different rules for better understanding by the end users. The study findings will help the researchers, policy makers and farmers in predicting/forecasting the crop yield in advance for market dynamics.

References
  1. Abdullah, A., Brobst, S and M.Umer M. 2004. The case for an agri data ware house: Enabling analytical exploration of integrated agricultural data. Proceedings of IASTED International Conference on Databases and Applications. Austria. Feb.
  2. Abdullah, A., Brobst, S, Pervaiz.I., Umer M.,and A.Nisar. 2004. Learning dynamics of pesticide abuse through data mining. Proceedings of Australian Workshop on Data Mining and Web Intelligence, New Zealand, January.
  3. Abdullah, A., Bulbul.R., and Tahir Mehmood. 2005. Mapping nominal values to numbers by data mining spectral properties of leaves. Proceedings of 3rd International Symposium on Intelligent Information Technology in Agriculture. Beijing, China. Oct, 2005.
  4. Basak J., Sudharshan, A., Trivedi D. and M.S.Santhanam . 2004. Weather Data Mining Using Independent Component Analysis. J. of Machine Learning Research 5: 239-253.
  5. Chi-Chung LAU and Kuo-Hsin HSIAO, 2005. Bayesian Classification For Rice Paddy interpretation. Paper presented in Conference on data mining held at China Tapei. December, 2005.
  6. Cunningham S.J., and G. Holmes. 2005. Developing innovative applications in agriculture using data mining. Proceedings of 3rd International Symposium on Intelligent Information Technology in Agriculture. Beijing, China. Oct, 2005.
  7. Han J and M Kamber, 2009. Data Mining Concepts and Techniques, Second Edition. Elsevier Publication. 285-305.
  8. Kiran Mai, C., Murali Krishna, I.V., and A.Venugopal Reddy, 2006. Data Mining Of Geo-spatial Database For Agriculture Related Application. Proceedings of Map India. New Delhi.
  9. Nain A. S., Dadhwal,V. K. and T. P. Singh, 2002 Real time wheat yield assessment using technology trend and crop simulation model with minimal data set. Current Science. 82(10): 1255-1258.
  10. Quinlan, J.R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.), Expert systems in the micro electronic age. Edinburgh University Press.
  11. Quinlan, J.R. (1983a). Learning efficient classification procedures and their application to chess endgames. In R.S. Michalski, J.G. Carbonell & T.M. Mitchell, (Eds.), Machine learning: An artificial intelligence approach. Palo Alto: Tioga Publishing Company.
  12. Rish, I., Hellerstein, J., and T. Jayram. An analysis of data characteristics that affect naive Bayes performance. Technical Report RC21993, IBM T.J. Watson Research Center, 2001.
  13. S. Diplaris, S., Symeonidis, A.L., Mitkas, P.A., Bano, G. and Z. Abas, 2001. A decision-tree-based alarming system for the validation of national genetic evaluations. Computers in agriculture. 52: 21-35.
  14. Sanghi A, R Mendelsohn, and A Dinar. The climate sensitivity of Indian agriculture, In Measuring the Impact of Climate Change on Indian Agriculture, edited by A. Dinar, et al. Washington DC: World Bank.
  15. World Bank Technical Paper No. 402 1998.
  16. Singh Gyanendra and Hukum Chandra, 2002. Production and economic factors growth in Indian Agriculture. Technical Bulletin no. CIAE/2002/91: 1-216.
Index Terms

Computer Science
Information Sciences

Keywords

Decision tree crop productivity ID3 algorithm climatic factors

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