A fuzzy modelling of decision support system for crop selection

The impact of climate change is expected to impact on high productivity and crop patterns agricultural sector in Malaysia. In this study, in order to handle the attribute of incomplete information, we use fuzzy modelling approach to overcome the uncertainty during the development of an agriculture web-based decision support system. Since data integration in the progeny test requires resolution of conflicts arising from the heterogeneity of data, we generalize decision algorithms classifier from incomplete information to provide simpler and more understandable information during user interaction. The analytical data about the rainfall pattern, soil structure of the area will partition data by taking full advantage of the incomplete information to achieve better performance. The contributions of this study are newly developed algorithms and analyses for planting material classification. The researcher reports the empirical results that may provide high return in planting material breeders in agriculture industry through effective policies of decision making.