Simulation of garden water dispersal controller using fuzzy expert system / Ku Shairah Jazahanim - Jazahanim, Ku Shairah (2006)
One of the most important elements in lawn maintenance is the moisture adequacy. For this reason, irrigation, done by manual or automated sprinkler system, has been applied. However, both systems may use excessive amount of water and the amount dispersed may not be suitable for the moisture level of the lawn. Therefore, there is a need to develop an irrigation system that can measure and monitor the soil moisture through data acquired from the soil and also from the climatologic factors that will help to decide when to water and how much water is needed. This research demonstrates the usage of Fu2zy Logic in irrigation control system. The types of fijzzy inference that applied to the irrigation system were Mandani with normal subsets, Mandani with hedges, Sugeno with normal subsets and Sugeno with hedges. The first step applied is the element identification involves in the irrigation process which was determined by consulting the relevant expertise and literature. Once the actual rules and frizzy sets are determined, the comparison of the conventional irrigation system with all four frizzy inference methods was conducted with each other. The intention is to see which system is better in optimizing water usage. Lastly, a simulation system was built to demonstrate the soil moisture content of the lawn, the percentage pattern of soil moisture and daily data involved in the system. This project was restricted to the Bermuda Turf grass characteristic, the loam soil characteristic, 92.9m lawn sizes, pop-up spray head sprinkler, evapotranspiration (ET); the climatology factor and the soil moisture reading from tensiometer. This project is significant to the irrigation industry whereby, new irrigation product can be produced using intelligent systems. From the comparison made, it was shown that the frizzy expert irrigation system performed better based on the lower annual average water usage for the whole year recorded. The most effective frizzy inference method applied was the Mandani style with normal subsets which uses the least amount of water. The result of this research shows promise for friture R&D in intelligent irrigation system either for individual or enterprise level management of plants.