The week's pick
Random Articles
Reseach Article
Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 99 - Number 12 |
| Year of Publication: 2014 |
| Authors: Manpreet Kaur, Heena Gulati, Harish Kundra |
10.5120/17422-8273
|
Manpreet Kaur, Heena Gulati, Harish Kundra . Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications. International Journal of Computer Applications. 99, 12 ( August 2014), 1-3. DOI=10.5120/17422-8273
Abstract
In agriculture crop price analysis, Data mining is emerging as an important research field. In this paper, we will discuss about the applications and techniques of Data mining in agriculture. There are various data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) which are used for very recent applications of Data Mining techniques. This paper will consider the problem of price prediction of crops. Price Prediction, nowadays, has become very important agricultural problem which is to be solved only based on the available data. Data Mining techniques can be used to solve this problem. This work is based on finding suitable data models that helps in achieving high accuracy and generality for price prediction. For solving this problem, different Data Mining techniques were evaluated on different data sets.
References
- Wu X, Kumar V, Quilan JR, Ghosh J, Yang Q, Motoda H, McLanchlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D, Top 10 algorithms in data mining. Knowl Inf Syst 14 : 1-37, 2008.
- Georg Ruß, Rudolf Kruse, Martin Schneider, and Peter Wagner. Estimation of neural network parameters for wheat yield prediction. In Max Bramer, editor, Artificial Intelligence in Theory and Practice II, volume 276 of IFIP International Federation for Information Processing, 109–118. Springer, July 2008.
- A. Mucherino, P. Papajorgji, P. M. Pardalos, A Survey of Data Mining Techniques Applied to Agriculture, Operational Research: An International Journal 9(2), 121–140, 2009.
- M. Kovacevic, B. Bajat, B. Gajic, Soil Type Classification and Estimation of Soil Properties using Support Vector Machines, Geoderma 154(3–4), 340–347, 2010.
- Cover TM, Hart PE, Nearest Neighbor pattern classification. IEEE Trans Info Theory 13(1) : 21-27, 1967.
- J. Hartigan, Clustering Algorithms, John Wiles & Sons, New York, 1975.
- A. Mucherino, A. Urtubia, Consistent Bi clustering and Applications to Agriculture, IbaI Conference Proceedings, Proceedings of the Industrial Conference on Data Mining (ICDM10), Workshop "Data Mining in Agriculture" (DMA10), Berlin, Germany, 105-113, 2010.
- Fagerlund S Bird species recognition using Support Vector Machines. EURASIP J Adv Signal Processing, Article ID 38637, p 8, 2007.
- Holmgren P, Thuresson T Satellite remote sensing for forestry planning: a review. Scand J For Res 13(1):90–110, 1998.
- Das KC, Evans MD Detecting fertility of hatching eggs using machine vision II: Neural Network classifiers. Trans ASAE 35(6):2035–2041, 1992.
- Patel VC, McClendon RW, Goodrum JW Crack detection in eggs using computer vision and neural networks. Artif Intell Appl 8(2):21–31, 1994.
- Du C-J, Sun D-W Pizza sauce spread classification using colour vision and support vector machines. J Food Eng 66:137–145,2005.
- Karimi Y, Prasher SO, Patel RM, Kim SH Application of support vector machine technology for Weed and nitrogen stress detection in corn. Computer Electronics Agriculture 51:99–109, 2006.
- Verheyen K, Adriaens D, Hermy M, Deckers S High-resolution continuous soil classification using morphological soil profile descriptions. Geoderma 101:31–48, 2001.
- Meyer GE, Neto JC, Jones DD, Hindman TW Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput Electronics Agric 42:161–180, 2004.
- Camps-Valls G, Gomez-Chova L, Calpe-Maravilla J, Soria- Olivas E, Martin-Guerrero JD, Moreno J Support Vector Machines for crop classification using hyperspectral data. Lect Notes Comp Sci 2652:134–141 , 2003.
- Shahin MA, Tollner EW, McClendon RW Artificial intelligence classifiers for sorting apples based on watercore. J Agric Eng Res 79(3):265–274, 2001.
- A. Mucherino, A. Urtubia, Feature Selection for Datasets of Wine Fermentations, I3M Conference Proceedings, 10th International Conference on Modeling and Applied Simulation (MAS11), Rome, Italy, September 2011.
- Riul A Jr, de Sousa HC, Malmegrim RR, dos Santos DS Jr, Carvalho ACPLF, Fonseca FJ, Oliveira Jr ON, Mattoso LHC Wine classification by taste sensors made from ultra-thin films and using Neural Networks. Sens Actuators B98:77–82, 2004.
- Brudzewski K, Osowski S, Markiewicz T Classification of milk by means of an electronic nose and SVM neural network. Sens Actuators B98:291–298, 2004.
Index Terms
Keywords