Thematic Fuzzy Prediction of Weed Dispersal Using Spatial Dataset

Abstract

This paper demonstrates the framework and methodology of how weed population dynamics can be predicted using rule-base fuzzy logic as applied to GIS spatial image. Parthenium weed (parthenium hysterophorus L.) infestation in the Central Queensland region poses a serious threat to the environment and to the economic viability of the infested areas. Government agencies have taken steps to control and manage existing infestation and to curb future spread of this noxious weed. One of the tools used in these strategies is the prediction of parthenium weed population. Conventional weed forecasting methods utilises discrete values in exponential models and linear algorithms extensively. Attempts at predicting weed dispersal relied heavily on accuracy of the original charts or images to yield reasonable results. Using these methods, results of weed population forecasting are only as reliable as the data originally provided. This paper demonstrates that by using GIS spatial image categorised into themes, a fuzzy logic based forecasting methodology can be performed. Fuzzy logic is best suited to this type of problem because of its ability to handle approximate data