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In the age of climate change, increasing populations and more limited resources, efficient agricultural production is being sought by farmers across the world. In the case of smallholder farms with limited capacity to cope with years of low production, this is even more important. To help to achieve this aim, data analytics and decision support systems are being used to an ever greater extent. For rice/shrimp farmers in the Mekong Delta, Vietnam, trying to tune the conditions so that both crops can be successfully grown simultaneously is an ongoing challenge. In this paper, the design and development of a smartphone app, from a well researched Bayesian Belief Network, is described. This now gives farmers the ability to make better informed planting and harvesting decisions. The app has been initially well received by water management practitioners and farmers alike.
|Title of host publication||Computational Science – ICCS 2020|
|Subtitle of host publication||20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part II|
|Editors||Valeria V. Krzhizhanovskaya, Gabor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sergio Brissos, João Teixeira|
|Place of Publication||Cham|
|Number of pages||11|
|Publication status||Published - 2020|
|Event||20th International Conference on Computational Science - Amsterdam, Netherlands|
Duration: 3 Jun 2020 → 5 Jun 2020
Conference number: 20th
|Name||Lecture Notes in Computer Science (LNCS)|
|Conference||20th International Conference on Computational Science|
|Abbreviated title||ICCS 2020|
|Period||3/06/20 → 5/06/20|
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