Implementing fuzzy modeling of decision support for crop planting management

This paper reports the empirical results that provide high return in planting material breeders in agriculture industry through effective policies of decision making. The analytical data about the rainfall pattern, soil structure of the planting crop will partition data by taking full advantage of the incomplete information to achieve better performance. Ignoring uncertain and vague nature of real world will undoubtedly eliminate substantial information. In order to handle the attribute of incomplete information, several fuzzy modeling approach has been proposed, which support the fuzziness at the attribute level. Then, we generalize decision algorithms that provide simpler and more understandable classifier to optimally retrieve the information based on user interaction. The proposed method leads to smaller decision tree and as a consequence better test performance in planting material classification.