Agricultural decision support system using data mining for farmers

The estimation of agricultural yield is a challenging and essential task for every farmer. Since the very old times, agriculture has always been the most important means of livelihood both in Turkey and all around the world. There are many factors that directly affect the efficiency in agriculture such as climatic features, use of water resources, proper and timely use of pesticides and fertilizers. Computer-based systems are needed to transform agriculture data into tangible information. Data mining involves certain methods of obtaining or inferring meaningful and otherwise-unknown information from the data. With the increasing significance of precision agricultural practices, farmers have become inclined to be engaged in a more conscious strategy of agriculture. In this study, barley crop data received from İzmir Menemen Provincial Directorate of Agriculture was carefully organized and evaluated with the classification algorithms in the SPSS Clementine software. CHAID and CR&T algorithms were employed and major factors that affect crop yield were defined. Based on these, a decision support system has been developed for farmers to forecast both harvest season and crop yield.