Development of a Data Mining Application for Agriculture Based on Bayesian Networks

  • Jiejun Huang
  • Yanbin Yuan
  • Wei Cui
  • Yunjun Zhan
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 258)

Data mining is a process by which the data can be analyzed so as to generate useful knowledge. It aims to use existing data to invent new facts and to uncover new relationships previously unknown even to experts. Bayesian network is a powerful tool for dealing with uncertainties, and has a widespread use in the area of data mining. In this paper, we focus on development of a data mining application for agriculture based on Bayesian networks. Let features (or objects) as variables or the nodes in Bayesian network, let directed edges present the relationships between features, and the relevancy intensity can be regarded as confidence between the variables. Accordingly, it can find the relationships in the agricultural data by learning a Bayesian network. After defining the domain variables and data preparation, we construct a model for agricultural application based on Bayesian network learning method. The experimental results indicate that the proposed method is feasible and efficient, and it is a promising approach for data mining in agricultural data.


data mining Bayesian network model agriculture 


  1. Abdullah A, Hussain A. Data mining a new pilot agriculture extension data warehouse. Journal of Research and Practice in Information Technology, 2006, 38(3): 229-248Google Scholar
  2. Andujar J M, Aroba J, et al. Contrast of evolution models for agricultural contaminants in ground waters by means of fuzzy logic and data mining. Environmental Geology, 2006, 49 (3): 458-466CrossRefGoogle Scholar
  3. Bajwa S G, Bajcsy P, Groves P, Tian L F. Hyperspectral image data mining for band selection in agricultural applications. Transactions of the American Society of Agricultural Engineers, 2004, 47(3): 895-907CrossRefGoogle Scholar
  4. Benferhat S, Cavarroc M, Jeansoulin R. Modeling landuse changes using bayesian networks. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, 2004, 615-620Google Scholar
  5. Braimoh A k, Vlek Paul L, Stein Alfred. Land Evaluation for Maize Based on Fuzzy Set and Interpolation, Environmental Management, 2004, 33(2): 226-238CrossRefPubMedGoogle Scholar
  6. Cheng J, Greiner R, Kelly J, et al. Learning Bayesian networks from data: An informationtheory based approach. Artificial Intelligence, 2002, 137(1-2): 43-90CrossRefGoogle Scholar
  7. Cooper G F, Herskovits E A. Bayesian method for the induction of Bayesian networks from data. Machine Learning, 1992, 9: 309-347Google Scholar
  8. Ghar M A, Renchin T, Tateishi R, Javzandulam T. Agricultural land monitoring using a linear mixture model. International Journal of Environmental Studies, 2005, 62(2): 227-234CrossRefGoogle Scholar
  9. Gu Y, Peiris D R, Crawford J, et al. An application of belief networks to future crop production. Proceedings of the 10th Conference on Artificial Intelligence for applications, San Antonio, Texas, 1994, 305-309Google Scholar
  10. Heckerman D. Bayesian Network for data mining, Data mining and knowledge discovery, 1997, 1: 79-119CrossRefGoogle Scholar
  11. Helman P, Veroff R, Atlas S, et al. A Bayesian network classification methodology for gene expression data. Journal of Computational Biology, 2004, 11(4): 581-615CrossRefPubMedGoogle Scholar
  12. Huang J J, Pan H P, Wan Y C. An algorithm for cooperative learning of Bayesian network structure from data. Lecture Notes in Computer Science, 2005, 3168: 86-94CrossRefGoogle Scholar
  13. Kalogirou S. Expert systems and GIS: An application of land suitability evaluation. Computers, Environment and Urban Systems, 2002, 26(2-3): 89-112CrossRefGoogle Scholar
  14. Lee S W, Kerschberg L. Methodology and life cycle model for data mining and knowledge discovery in precision agriculture. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1998, 3: 2882-2887Google Scholar
  15. Lein J K. Applying evidential reasoning methods to agricultural land cover classification International Journal of Remote Sensing, 2003, 24 (21): 4161-4180CrossRefGoogle Scholar
  16. Liu Y, Jiao L. Model of land suitability evaluation based on computational intelligence. Wuhan Daxue Xuebao (Xinxi Kexue Ban), 2005, 30(4): 283-287 (in Chinese)Google Scholar
  17. Martin de Santa Olalla F, Dominguez A, Ortega F, et al. Bayesian networks in planning a large aquifer in Eastern Mancha, Spain. Environmental Modelling and Software, 2007, 22 (8): 1089-1100CrossRefGoogle Scholar
  18. Tari F. A Bayesian Network for predicting yield response of winter wheat to fungicide programmes. Computer and electronics in agriculture, 1996, 15: 111-121CrossRefGoogle Scholar
  19. Tsamardinos I, Brown L, Aliferis C. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning, 2006, 65(1): 31-78CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Jiejun Huang
    • 1
  • Yanbin Yuan
    • 1
  • Wei Cui
    • 1
  • Yunjun Zhan
    • 1
  1. 1.School of Resource and Environmental EngineeringWuhan University of TechnologyChina

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