Developing a Decision Support App for Computational Agriculture

Andrew Lewis, Marcus Randall, Ben Stewart-Koster

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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.
Original languageEnglish
Title of host publicationComputational Science – ICCS 2020
Subtitle of host publication20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part II
EditorsValeria V. Krzhizhanovskaya, Gabor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sergio Brissos, João Teixeira
Place of PublicationCham
Number of pages11
ISBN (Electronic)978-3-030-50417-5
ISBN (Print)978-3-030-50416-8
Publication statusPublished - 2020
Event20th International Conference on Computational Science - Amsterdam, Netherlands
Duration: 3 Jun 20205 Jun 2020
Conference number: 20th

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Computational Science
Abbreviated titleICCS 2020
Internet address


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