An analysis of pesticide use for cotton production through data mining:the case of Nazilli

Data mining involves certain methods of obtaining or inferring meaningful information unknown within the data. Besides of the fact that it is used in the fields of health, marketing, banking; it is also utilized in agriculture. With the increasing significance of precision agriculture practices, farmers have become inclined to be engaged in a more conscious agriculture. Farmers use pesticides to destroy a disease or hazard on plants. Nevertheless, in some researches, it has been revealed that pesticides have harmful effects on human health, environment and plant. Although many farmers are aware of the risks of excessive use of agricultural pesticides, they still use them to get a faster yield and avoid financial loss. The goal of this study is to create a model for cotton growers that will indicate the optimum amount of pesticides to apply for the maximum yield rate. The ultimate aim is the modeling decision tree based classification algorithms on the data and the observation of the results. The cotton planted fields and used pesticides data received from Aydın Nazilli District Directorate of Agriculture was organized, it was evaluated with the help of classification algorithms in SPSS Clementine software. C5.0, Classification And Regression Tree (C&RT) and Chaid decision tree algorithms that are the most preferred data mining methods are employed in this study. Thereby, it was observed that there exist some certain suggestive differences between the fertility obtained from the product and the pesticide used.