Abstract
Bayesian network is a powerful tool to represent and deal with uncertain knowledge. There exists much uncertainty in crop or animal disease. The construction of Bayesian network need much data and knowledge. But when data is scarce, some methods should be adopted to construct an effective Bayesian network. This paper introduces a disease diagnosis model based on Bayesian network, which is two-layered and obeys noisy-or assumption. Based on the two-layered structure, the relationship between nodes is obtained by domain knowledge. Based on the noisy-model, the conditional probability table is elicited by three methods, which are parameter learning, domain expert and the existing certainty factor model. In order to implement this model, a Bayesian network tool is developed. Finally, an example about cow disease diagnosis was implemented, which proved that the model discussed in this paper is an effective tool for some simple disease diagnosis in crop or animal field.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Chard T. Qualitative probability versus quantitative probability in clinical diagnosis: a study using a computer simulation. Med Decis Making. 1991 Jan-Mar;11(1):38–41.
David J.Spiegelhalter. Bayesian Analysis in Expert Systems, Statistical Science, 1993. Volume 8, Issue 3: 219–247.
E.Charles, J.Kahn, etc. Construction of a Bayesian network for mammographic diagnosis of breast cancer, Comut. Biol. Med, 1997:19–29.
F.trai..A Bayesian network for predicting yield response of winter wheat to fungicide programs, Computers and electronics in agriculture. 1996: 111–121.
Kevin B.Korb, Ann E.Nicholson. Bayesian Artificial Intelligence, CRC Press.2006:.225–260
Kristian Kristensen etc, The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides, Computers and Electronics in Agriculture, 2002(33):197–217
Nevin Lianwen Zhang. Exploiting causal independence in Bayesian network inference, Journal of artificial intelligence, 1996: 301–328.
P.J.F Lucas. Bayesian network modeling through qualitative patterns. Artificial Intelligence, 2005: 233–263.
P.J.F Lucas. Certainty-Factor-Like structures in Bayesian belief networks, Knowledge-based systems,2001: 327–335.
P.Larranaga, S.Moral. Probabilistic graphical models in artificial intelligence. Applied soft computing. 2008:1–18.
Radim Jirousck. Constructing probabilistic models, International journal of medical informatics 1997(45): 9–18.
Wang ronggui etc, From Certainty Factor Model to Bayesian Network. computer science, 2004, 31(10).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this paper
Cite this paper
Yu, H., Chen, G., Liu, D. (2009). A SIMPLIFIED BAYESIAN NETWORK MODEL APPLIED IN CROP OR ANIMAL DISEASE DIAGNOSIS. In: Li, D., Zhao, C. (eds) Computer and Computing Technologies in Agriculture II, Volume 2. CCTA 2008. IFIP Advances in Information and Communication Technology, vol 294. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0211-5_25
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0211-5_25
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0210-8
Online ISBN: 978-1-4419-0211-5
eBook Packages: Computer ScienceComputer Science (R0)