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ICT based Data mining Approach for Extracting Patterns from Socioeconomic Databases to Differentiate Huge and Tiny Paddy Yields in Tamil Nadu
Abstract
In the Current scenario, agricultural society works with huge number of data. Progression and recovery of important data in this large quantity of agricultural information is essential. The information and Communications Technologies (ICT) assists computerization of extracting most important data in an attempt to obtain facts and trends, which facilitate the eradication of manual assignment and directly extract data from electronic sources, relocate to protected electronic system of records which will make possible cost reduction, high yield and better market value. This study aims to characterize households with huge and low paddy yields supported the Department of Economics and statistics Chennai, Tamil Nadu Survey information from 2011 and 2012 employing a data mining approach. Here we tend to use some techniques like decision trees, association rules, and classification rules. The results show that domestics with high paddy yields area unit those with the capability to come up with economic gain through the exploitation of their creation and agricultural benefits. Households produce low paddy yields area unit related to production loss before harvest which ends in food insecurity.
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References
Yethiraj N G, “Applying Data Mining Techniques In The Field Of Agriculture And Allied Sciences”, International Journal of Business Intelligents ISSN: 2278-2400, Vol 01, Issue 02, December 2012.
Ramesh D, Vishnu Vardhan B., “Data Mining Techniques and Applications to Agricultural Yield Data”, IJARCCE, Vol. 2, Issue 9, September 2013.
Mucherino, A., Papajorgji, P., & Pardalos, P. (2009), “Data mining in agriculture” (Vol. 34), Springer.
Srikant, R V Q & Agrawal, R (1997, August), “Mining Association Rules with Item Constraints. In KDD” (Vol. 97, pp. 67-73).
Agricultural-Statistics-At-Glance2014-india.
Beniwal, S. & Arora, J. (2012), “Classification and Feature selection techniques in data mining”, International Journal of Engineering Research& Technology (IJERT).
Lior Rokach, Oded Maimon, “Clustering Methods”,Chap-15MINAG, Plano Estratégico Para Desenvolvimento do Sector Agrário 2011 -2020 (PEDSA), M.d.
Agricultura, Editor. 2011, Ministério da Agricultura: Maputo. p. 1-69.
Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth, From Data Mining to Knowledge Discovery in Databases. Ai Magazine, 1996: p. 37-54.
Han, J. and M. Kamber, Data Mining, Concept and Techniques. Second ed. Data Management Systems, ed. M.R. Jim Gray. 2006, San Francisco: Diane Serra.
Mucherino, A., P.J. Papajorg, and P.M. Pardalos, eds. Data Mining in Agriculture. Springer Optimization and Its Applications, ed. D.-Z. Du. 2009, Springer: London. 274.
Mucherino, A., P. Papajorgji, and P. Pardalos, A survey of data mining techniques applied to agriculture. Operational Research, 2009. 9(2): p. 121-140.
Mucherino, A. and G. Rub. Recent Developments in Data Mining and Agriculture. in In-dustrial Conference on Data Mining 2011. 2011. New York, NY, USA: IBaI Publishing.
Drummond, S., A. Joshi, and K.A. Sudduth. Application of neural networks: precision farming. in Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on. 1998.
Serele, C.Z., et al. Corn yield prediction with artificial neural network trained using air-borne remote sensing and topographic data. In Geosciences and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International. 2000.
Ruß, G., Data Mining of Agricultural Yield Data: A Comparison of Regression Models Advances in Data Mining. Applications and Theoretical Aspects, P. Perner, Editor. 2009, Springer Berlin / Heidelberg. p. 24-37.
Ruß, G. and R. Kruse, Exploratory Hierarchical Clustering for Management Zone Delineation in Precision Agriculture Advances in Data Mining. Applications and Theoretical Aspects, P. Perner, Editor. 2011, Springer Berlin / Heidelberg. p. 161-173.
Waheed, T., et al., Measuring performance in precision agriculture: CART—A decision tree approach. Agricultural Water Management, 2006. 84(1–2): p. 173-185.
Ekasingh, B., et al., A data mining approach to simulating farmers' crop choices for integrated water resources management. Journal of Environmental Management, 2005. 77(4): p. 315-325.
Ngamsomsuke, K. and B. Ekasingh. Modeling Farmers' Crop Choice Using Data Mining Approach: A Revisit. in 2 nd International Conference on Asian Simulation and Modeling (ASIMMOD) 2007. Chiang Mai, Thailand: The Agricultural Cooperative Farmer of Thai-land.
Ekasingh, B. and K. Ngamsomsuke, Searching for simplified farmers' crop choice models for integrated watershed management in Thailand: A data mining approach. Environmental Modelling & Software, 2009. 24(12): p. 1373-1380.
Dunham, M.H., ed. Data Mining: Introductory and Advanced Topics. ed. M.J. Harton. 2003, Prentice Hall: London. 315.
H.Witten, I. and E. Frank, eds. Data Mining: Praticam Machine Learning Tools and Tech-niques. Second ed. The Morgan Kaufmann Series in Data Management Systems, ed. M.R. Jim Gray. 2005, Morgan Kaufmann: San Francisco. 525.
Uaiene, R.N., Introduction of New Agricultural Technologies and Marketing Strategies in Central Mozambique, in Research Report:, R. Report, Editor. 2006, Institute of Agricultural Research of Mozambique Directorate of Training, Documentation, and Technology Transfer: Maputo. p. 31.
Hall, M., et al. The WEKA Data Mining Software: An Update. in SIGKDD Explorations, . 2009.
Hall,M. http://weka.sourceforge.net/packageMetaData/hotSpot/index.html. 2011 [cited 2012 7/01/2012].
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