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.
Cite this paper
Mulatu, W. B. , Bedasa, M. F. and Terefa, G. K. (2020). Prediction of Wheat Rust Diseases Using Data Mining Application. Open Access Library Journal, 7, e6717. doi: http://dx.doi.org/10.4236/oalib.1106717.
John Dodds, Director of Research, International Maize and Wheat Improvement Center (CIMMYT), Safeguarding the World’s Wheat Harvests from Stem Rust: A Global Initiative, Apdo. http://www.cimmyt.org
CSA (Central Statistical Agency) (2010) Large and Medium Scale Commercial Farms Sample Survey. Statistical Report on Area and Production of Crops, and Farm Management Practices. Statistical Bulletin 505. Addis Ababa.
Marasas, C.N., Smale, M. and Singh, R.P. (2004) The Economic Impact in Developing Countries of Leaf Rust Resistance Breeding in CIMMYT-Related Spring Bread Wheat. Economics Program.
Plant and Pest Diagnostic Clinic University of Nebraska-Lincoln 448 Plant Sciences Hall Lincoln, NE 68583-0722 (402) 472-2559 Publication Number: EC 1899.
International Center for Agricultural Research in the Dry Areas (ICARDA)—Research to Action—Strategies to Reduce the Emerging Wheat Stripe Rust Disease International Wheat Stripe Rust Symposium, Aleppo, Syria. 2011.
Sathiamoorthy, S., Ponnusamy, R. and Natarajan, M. (2018) Sugarcane Disease Detection Using Data Mining Techniques. International Journal of Research in Advent Technology, Special Issue, 296-301. http://www.ijrat.org
Namita, M. and Smitha, H. (2016) Application of Data Mining in Agriculture Field. International Journal of Computer Engineering and Applications, Special Issue, 94-105.
University of Nebraska-Lincoln Extension Educational Programs Abide with the Nondiscrimination Policies of the University of Nebraska-Lincoln and the United States Department of Agriculture, 2012.
Tamene, M., Chemeda, F. and Bekele, H. (2018) Analysis of Climate Variability Effects on Wheat Stem Rust (Puccinia graminis f. sp tritici) Epidemics in Bale and Arsi Zones of Oromia Regional State, Ethiopia. American Journal of Biological and Environmental Statistics, 4, 49-65. https://doi.org/10.11648/j.ajbes.20180402.12
Harding, A.C.J. (2009) Data Mining in Manufacturing: A Review Based on the Kind of Knowledge. Journal of Intelligent Manufacturing, 20, 501-521.
https://doi.org/10.1007/s10845-008-0145-x
Rao, D.R., Pellakuri, V., Tallam, S. and Harika, T. (2016) Performance Analysis of Classification Algorithms Using Healthcare Dataset. International Journal of Computer Science and Information Technologies, 6, 1103-1106.
Lakshmi, K.R. and Prem Kumar, S. (2013) Utilization of Data Mining Techniques for Prediction of Diabetes Disease Survivability. International Journal of Scientific & Engineering Research, 4, 933.
Vijaya Kumar, P. (2014) Development of Weather-Based Prediction Models for Leaf Rust in Wheat in the Indo-Gangetic Plains of India. Central Research Institute for Dryland Agriculture, Santoshnagar. https://doi.org/10.1007/s10658-014-0478-6
Siri, K. and Ha, K. (2006) Empirical Study on Applications of Data Mining Techniques in Healthcare. Journal of Computer Science, 2, 194-200.
https://doi.org/10.3844/jcssp.2006.194.200
Vijiyarani, S. and Sudha, S. (2013) Disease Prediction in Data Mining Technique— A Survey. International Journal of Computer Applications & Information Technology, 2, 2278-7720.
Rokach, L. and Maimon, O. (2015) Decision Trees, Data Mining and Knowledge Discovery Handbook. Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv.