Intelligent Water Dispersal Controller: Comparison between Mamdani and Sugeno Approaches

Abstract

This paper presents a comparison of fuzzy inference methods in intelligent water dispersal controller primarily focuses on grass watering. In irrigation system, measuring and monitoring soil moisture from the soil information and climatologic factors would determine the amount of water for sufficient soil moisture. Mamdani-style and Sugeno-style inference methods have been tested and evaluated using this information. These methods were tested on normal subsets. Fuzzy rules were determined based on three inputs namely; Bermuda Turf grass coefficient, evapotranspiration (FT) rate, and tensiometer data. The result illustrated that the most convincing fuzzy inference method applied was the Mamdani-style compared to Sugeno-style. It was shown that the controller used less water in turf grass irrigation. Overall, both of the tested methods give significant result to the recognition of soil moisture level.