Integrating Machine Learning Tool to Improve DSS Design

This paper describes how a machine learning tool can be applied to decision support system. We have used fuzzy logic to enhance performance of DSS. Further, system developed is implemented in agriculture domain for selection of suitable crop. Selection of crop is complex process as it involves number of parameters where uncertainty is more common for example rainfall, suitable seeds, fertilizers, number of soil parameters, temperature, air quality, humidity, and so on. The present work focuses on soil parameters and few other parameters which support proper growth of crops. Fuzzy logic is applied to those parameters for handling data uncertainty. This is an attempt to suggest proper decision and reduce the burden by designing new DSS. Experimental set-up shows increased crop production up to 10-12%.