Efficiencies in farming practice in many parts of South East Asia can make substantial, positive differences to villages and communities. The use of automated decision-assistance tools such as Bayesian Belief Networks (BBNs) can help to accomplish this. For the problem described herein, farmers attempt to grow both rice and shrimp crops in the same physical area. The motivation becomes one of finding a set of conditions that minimises the probabilities of crop failures. In this work, we explore an existing BBN and determine a range of likely environmental scenarios and the factors that farmers can control to help improve the likelihood of harvesting successful rice and shrimp crops.
|Title of host publication||Proceedings of the 16th International Conference on Computer Applications 2018|
|Place of Publication||Yangon|
|Publisher||University of Computer Studies, Yangon|
|Number of pages||9|
|Publication status||Published - 2018|
|Event||International Conference on Computer Applications - Yangon, Myanmar|
Duration: 22 Feb 2018 → 23 Feb 2018
Conference number: 16th
|Conference||International Conference on Computer Applications|
|Abbreviated title||ICCA 2018|
|Period||22/02/18 → 23/02/18|
Lewis, A., Randall, M., Stewart-Koster, B., Dieu Anh, N., Burford, M., Condon, J., Van Qui, N., Huu Hiep, L., & Sammut, J. (2018). Explorations of a Bayesian Belief Network for the Simultaneous Farming of Rice and Shrimp Crops. In Proceedings of the 16th International Conference on Computer Applications 2018 (pp. 85-93). University of Computer Studies, Yangon.