Selecting Sprinklers in a Self-Propelled Center-Pivot Irrigation System Based on Calculated Performance Indicators Using Data Mining Algorithms

Water uniformity is affected by sprinklers in a self-propelled center-pivot irrigation system. Thus sprinklers acceptability is very important in water management of such systems. In this paper the objective was focused on the applications of data mining algorithms for selecting a sprinkler based on calculated performance indicators like coefficient of uniformity, distribution uniformity in the low quarter of center pivot irrigation system, application efficiency, application efficiency in the low quarter, gross depth of water applied and the average of weighted depth in low quarter of caught water applied from a center pivot irrigation system. The tested sprinkler types were NelsonD3000 Sprayhead-3TN, Nelson R3000 Rotator-3TN, Nelson S3000 Spinner-3TN, Senninger i-Wob and Senninger LDN. Various data mining classification techniques such as J48, Random tree and Naïve Bayes were utilized. The classification was done by using Weka open source tool. The results were analyzed using training and testing data sets. Random tree gives the highest correctly classified percentage of 100%. Meanwhile, J48 and Naive Bayes give correctly classified percentage of 80% and 60%, respectively for testing data set. This study concludes that the irrigation data mining classification technique become highly active research to select sprayers in a center pivot irrigation system.