SLGP Header

Analysis of Data Mining Techniques for Agriculture Data

IJCSEC Front Page

Abstract
Data mining is the process of extracting important and useful information from large sets of data. The goal of the data mining process is to extract knowledge from an existing data set and transform it into a unique human understandable format for some advance use. Data mining in agriculture is in relation to novel research field. Some efficient techniques can be developed and tailored for solving complex agricultural problems using data mining. Agriculture and allied activities constitute the single largest component of India’s gross domestic product, contributing nearly 25% of the total and nearly 60% of Indian population depends on this life’s precise profession. Due to vagaries of climate factors the agricultural productivities in India are continuously decreasing over a decade. The reasons for this were studied mostly using regression analysis. In this paper an attempt has been made to compile the research findings of different researchers who used agriculture data. This paper summarizes the application of data mining techniques such as k-means, bi clustering, k nearest neighbor, Neural Networks, Support Vector Machine and Naïve Bayes Classifier in the agriculture field.
Keywords:Agriculture, Data mining, k-means, bi clustering, k nearest neighbor, Artificial Neural Network, Support Vector Machine, Naïve Bayesian Classifier.

References:

  1. Hetal Patel & Dharmendra Patel. (2014). A Brief survey of Data Techniques Applied to Agricultural Data, International Journal of Computer Applications, 95(3).
  2. Mucherino, A., Papajorgji, P., & Pardalos, P.(2009). Data mining in agriculture (Vol. 34). Springer.
  3. Bhargavi, P, & Jyothi, S. (2009). Applying Naive Bayes data mining technique for classification of agricultural land soils. International journal of computer science and network security, 9(8), 117-122.
  4. Jay Gholap. (2012). Performance tuning of j48 algorithm for prediction of soil fertility. Asian Journal of Computer Science And Information Technology 2: 8 (2012) 251– 252.
  5. Megala, S., & Hemalatha, M. (2011). A Novel Datamining Approach to Determine the Vanished Agricultural Land in Tamilnadu. International Journal of Computer Applications, 23.
  6. D Ramesh, B Vishnu Vardhan, (2013). Data Mining Techniques and Applications to Agricultural Yield Data. International Journal of Advanced Research in Computer and Communication Engineering 2(9).
  7. V. Ramesh and K. Ramar, 2011. Classification of Agricultural Land Soils: A Data Mining Approach. Agricultural Journal, 6: 82-86.
  8. Verheyen, K., Adriaens, D., Hermy, M., & Deckers, S. (2001). High-resolution continuous soil classification using morphological soil profile descriptions. Geoderma, 101(3), 31-48.
  9. Meyer, G. E., Camargo Neto, J., Jones, D. D., & Hindman, T. W. (2004). Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and electronics in agriculture, 42(3), 161-180.
  10. Leemans, V., & Destain, M. F. (2004). A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering, 61(1), 83-89.
  11. K.A. Klise and S.A. McKenna.(2006). Water Quality Change Detection: Multivariate Algorithms. Proceedings of SPIE 6203, Optics and Photonics in Global Homeland Security II, T.T. Saito,D. Lehrfeld (Eds.)
  12. Tellaeche, A., BurgosArtizzu, X. P., Pajares, G., & Ribeiro, A. (2007). A vision-based hybrid classifier for weeds detection in precision agriculture through the Bayesian and Fuzzy k-Means paradigms. In Innovations in Hybrid Intelligent Systems (pp. 72-79). Springer Berlin Heidelberg.
  13. Urtubia, A., Pérez-Correa, J. R., Soto, A., & Pszczolkowski, P. (2007). Using data mining techniques to predict industrial wine problem fermentations. Food Control,18(12), 1512-1517.
  14. Rajagopalan, B., & Lall, U. (1999). A k–nearest-neighbor simulator for daily precipitation and other weather variables. WATER RESOURCES RESEARCH,35(10), 3089-3101.
  15. Elizondo, D. A., McClendon, R. W., & Hoogenboom, G. (1994). Neural network models for predicting flowering and physiological maturity of soybean. Transactions of the ASAE (USA).
  16. Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modeling & software, 15(1), 101-124.
  17. Camps-Valls, G., Gómez-Chova, L., Calpe-Maravilla, J., Soria-Olivas, E., Martín-Guerrero, J. D., & Moreno, J. (2003). Support vector machines for crop classification using hyper spectral data. In Pattern recognition and image analysis(pp. 134-141). Springer Berlin Heidelberg.
  18. Tripathi, S., Srinivas, V. V., & Nanjundiah, R. S. (2006). Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology, 330(3), 621-640.
  19. Mucherino, A., Papajorgji, P., & Pardalos, M.P.(2009). A Survey of Data Mining techniques applied to agriculture. Spinger.
  20. Abello J, Pardalos PM, Resende M (2002) Handbook of massive data sets. Kluwer, New York.
  21. Mucherino A, Papajorgji P, Pardalos PM (2009) Data mining in agriculture. Springer, New York (in press).
  22. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37.
  23. Yethiraj N G (2012) Applying Data Mining Techniques In the field of Agriculture and Allied Sciences, International Journal of Business Intelligent.