The week's pick
Reseach Article
A Brief survey of Data Mining Techniques Applied to Agricultural Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 95 - Number 9 |
| Year of Publication: 2014 |
| Authors: Hetal Patel, Dharmendra Patel |
10.5120/16620-6472
|
Hetal Patel, Dharmendra Patel . A Brief survey of Data Mining Techniques Applied to Agricultural Data. International Journal of Computer Applications. 95, 9 ( June 2014), 6-8. DOI=10.5120/16620-6472
Abstract
As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy c-means. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.
References
- Han, J, Kamber, M. , & Pei, J. (2006). Data mining: concepts and techniques. Morgan kaufmann.
- http://www. publishyourarticles. net/knowledge-hub/essay/essay-on-the-importance-of-agriculture-in-the-indian-economy. html
- Fayyad, U. , Piatetsky-Shapiro, G. , & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
- Mucherino, A. , Papajorgji, P. , & Pardalos, P. (2009). Data mining in agriculture (Vol. 34). Springer.
- Beniwal, S. , & Arora, J. (2012). Classification and feature selection techniques in data mining. International Journal of Engineering Research & Technology (IJERT), 1(6).
- Lior Rokach, Oded Maimon. Clustering Methods. Chap-15
- Xu, R & Wunsch, D (2005). Survey of clustering algorithms. Neural Networks, IEEE Transactions on, 16(3), 645-678.
- Periklis Andritsos Data Clustering Techniques. University of Toronto, Department of Computer Science. ftp://ftp. cs. toronto. edu/csrg-technical-reports/443/depth. pdf
- Srikant, R V Q & Agrawal, R (1997, August). Mining Association Rules with Item Constraints. In KDD (Vol. 97, pp. 67-73).
- Agrawal, R. , Imieli?ski, T. , & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In ACM SIGMOD Record (Vol. 22, No. 2, pp. 207-216). ACM.
- Zaki, M J (1999). Parallel and distributed association mining: A survey. IEEE concurrency, 7(4), 14-25.
- 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.
- 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.
- Megala, S. , & Hemalatha, M. (2011). A Novel Datamining Approach to Determine the Vanished Agricultural Land in Tamilnadu. International Journal of Computer Applications, 23.
- 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).
- V. Ramesh and K. Ramar, 2011. Classification of Agricultural Land Soils: A Data Mining Approach. Agricultural Journal, 6: 82-86.
- Verheyen, K. , Adriaens, D. , Hermy, M. , & Deckers, S. (2001). High-resolution continuous soil classification using morphological soil profile descriptions. Geoderma, 101(3), 31-48.
- 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.
- 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.
- 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. )
- 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.
- 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.
- Rajagopalan, B. , & Lall, U. (1999). A k–nearest-neighbor simulator for daily precipitation and other weather variables. WATER RESOURCES RESEARCH,35(10), 3089-3101.
- 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).
- 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 modelling & software, 15(1), 101-124.
- 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 hyperspectral data. In Pattern recognition and image analysis(pp. 134-141). Springer Berlin Heidelberg.
- 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.
Index Terms
Keywords