Explorations of a Bayesian Belief Network for the Simultaneous Farming of Rice and Shrimp Crops

Andrew Lewis, Marcus Randall, Ben Stewart-Koster, N Dieu Anh, M Burford, J Condon, N Van Qui, L Huu Hiep, J Sammut

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Computer Applications 2018
Place of PublicationYangon
PublisherUniversity of Computer Studies, Yangon
Number of pages9
Publication statusPublished - 2018
EventInternational Conference on Computer Applications - Yangon, Myanmar
Duration: 22 Feb 201823 Feb 2018
Conference number: 16th


ConferenceInternational Conference on Computer Applications
Abbreviated titleICCA 2018
Internet address


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