Prediction of Wheat Rust Diseases Using Data Mining Application

Abstract

Stem rust caused by Puccinia graminis f.sp. tritici and stripe rust caused by Puccinia striiformis are the most destructive wheat rust diseases when environment conditions are favorable in regions where wheat crops are grown. An early prediction mechanism can play a great role in forecasting the occurrence of the rust disease. It assists proactive control and early decision making. However, in the absence of prediction mechanism of wheat rust disease hurried wheat production yield loss more. Hence, to overcome these issues, this study was conducted to develop wheat stripe rust and stem rust diseases prediction model using data mining application. The meteorological and disease data for the year 2010 to 2019 of national meteorological agency southern Oromia region Bale Robe service center and Oromia Seed enterprise Sinana farm unit II were obtained and used for the study. Two bread wheat varieties, namely Kakaba and Danda’a were involved in the study. Fist, daily meteorological data mean values of three-day consecutive interval were computed. Then, mean value of meteorological data has been integrated with disease incidence and severity date observed during the most critical infection period (mid-august to November-30) was used to develop the model. WEKA software machine learning tool with J48 decision tree algorithm was used for data preprocess and experiments. It is open source software containing various machine learning algorithms for data mining tasks. The study results showed that, stripe rust predictive model trained and tested with using training set test option achieved accuracy 75.70% and stem rust predictive model achieved accuracy 90.045% with cross-validation fold 10 test option. The results found were much promised to forecast occurrence of wheat stripe and stem rust disease.