Application of Belief Networks to Future Crop Production

Gu, Y., Ramanee Peiris, D., Crawford, J. W. , McNicol, J. W., Marshall, B. and Jefferies, R. A. (1994) Application of Belief Networks to Future Crop Production. In: 10th Conference on Artificial Intelligence for Applications, San Antonio, TX, USA, 1-4 Mar 1994, pp. 305-309.

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Abstract

Bayesian belief networks are shown to be natural and efficient knowledge representation tools for modelling and manipulating uncertainties in developing expert systems. They provide a basis for probabilistic inference, to calculate the changes in probabilistic belief as new evidence is obtained. However, their use in real problem domains is hampered by the difficulties facing the construction of such belief networks, particularly in domains where neither sufficient data nor human expertise is available. In this paper, we will show that this problem can be circumvented by exploiting knowledge from existing mathematical models. An application of belief networks to assess the impact of climate change on potato production is used as an illustration. We will show how the uncertainty of future climate change, variability of current weather and the knowledge about potato development can be combined in a belief network, which provides an aid for policy makers in agriculture. The model is tested using synthetic weather scenarios. The results are compared with those obtained from a conventional mathematical model.

Item Type:Conference Proceedings
Additional Information:Proceedings of the Conference on Artificial Intelligence Applications 1994, pages 305-309.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Crawford, Professor John
Authors: Gu, Y., Ramanee Peiris, D., Crawford, J. W., McNicol, J. W., Marshall, B., and Jefferies, R. A.
College/School:College of Social Sciences > Adam Smith Business School > Management
Journal Name:Proceedings of the Conference on Artificial Intelligence Applications
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