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Combining spatial data layers using fuzzy inference systems: application to an agronomic case study

Abstract : This paper presents an application of Fuzzy Logic, well known for its linguistic modeling ability, in a multi-criteria decision making framework applied to spatial data sets. The Fuzzy Logic is integrated in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual spatial information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented in an open source software called GeoFIS. The potential of the method is illustrated using a typical farming decision: the design of a nitrogen fertilization map within a vineyard. The vineyard is a Concord (Vitis labrusca) juice grape vineyard in the Lake Erie region of New York state. The vineyard manager and a local research/extension viticulturist both used the tool to generate a prescription nitrogen map based on their knowledge and spatial crop and soil information. The process captured different preferences between the two users (industry vs. research) and generated different prescription maps that reflected their differing objectives, knowledge and risk perception in vine management. Although applied to vineyard data, this decision tool has a wide potential application to agri-environmental (and other) spatial data sets.
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https://hal.inrae.fr/hal-02942751
Contributor : James Taylor <>
Submitted on : Friday, September 18, 2020 - 11:32:53 AM
Last modification on : Friday, June 11, 2021 - 5:12:08 PM

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Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

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Serge Guillaume, Terry Bates, Jean-Luc Lablée, Thom Betts, James Taylor. Combining spatial data layers using fuzzy inference systems: application to an agronomic case study. 6th International Conference on Geographical Information Systems Theory, Applications and Management, May 2020, Prague, Czech Republic. pp.62-71, ⟨10.5220/0009356000620071⟩. ⟨hal-02942751⟩

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