Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots

Version 1 : Received: 22 June 2021 / Approved: 23 June 2021 / Online: 23 June 2021 (11:03:08 CEST)

How to cite: Mendes, W.R.; Er-Raki, S.; Heeren, D.M.; Dutta, R.; Araújo, F.M.U. Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots. Preprints 2021, 2021060564 (doi: 10.20944/preprints202106.0564.v1). Mendes, W.R.; Er-Raki, S.; Heeren, D.M.; Dutta, R.; Araújo, F.M.U. Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots. Preprints 2021, 2021060564 (doi: 10.20944/preprints202106.0564.v1).

Abstract

Growing agricultural demands for the global population are unlocking the path to developing innovative solutions for efficient water management. Herein, an intelligent variable rate irrigation system (fuzzy-VRI) is proposed for rapid decision-making to achieve optimized irrigation in various delimited zones. The proposed system automatically creates irrigation maps for a center pivot irrigation system for a variable-rate application of water. Primary inputs are spatial imagery on remotely sensed soil moisture (SSM), soil adjusted vegetation index (SAVI), canopy temperature (CT), and nitrogen content (NI). To eliminate localized issues with soil characteristics, we used the crop nitrogen content map to provide a focused insight on issues related to water shortage. The system relates these inputs to set reference values for the rotation speed controllers and individual openings of each central pivot sprinkler valve. The results showed that the system can detect and characterize the spatial variability of the crop and further, the fuzzy logic solved the uncertainties of an irrigation system and defined a control model for high-precision irrigation. The proposed approach is validated through the comparison between the recommended irrigation and actual irrigation at two field sites, and the results showed that the developed approach gives an accurate estimation of irrigation with a reduction in the volume of irrigated water of up to 27% in some cases. Future research should implement the fuzzy-VRI real-time during field trials in order to quantify its effect on irrigation use, yield, and water use efficiency.

Subject Areas

Remote sensing data; variable rate irrigation; irrigation management; fuzzy systems; decision support tools; intelligent center pivot

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