Developing a decision support app for computational agriculture

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Lewis, A
Randall, M
Stewart-Koster, B
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2020
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Amsterdam, The Netherlands

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Abstract

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.

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Lecture Notes in Computer Science

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12138

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© Springer Nature Switzerland AG 2020. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com

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Artificial intelligence

Environmental engineering

Information and computing sciences

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Lewis, A; Randall, M; Stewart-Koster, B, Developing a decision support app for computational agriculture, Lecture Notes in Computer Science, 2020, 12138, pp. 551-561