Rice Leaf Blast Disease Detection Using Multi-Level Colour Image Thresholding


  • M.N. Abu Bakar Engineering Centre FTK, Universiti Malaysia Perlis
  • A.H. Abdullah School of Mechatronics Engineering, Universiti Malaysia Perlis
  • N. Abdul Rahim School of Mechatronics Engineering, Universiti Malaysia Perlis
  • H. Yazid School of Mechatronics Engineering, Universiti Malaysia Perlis
  • S.N. Misman Rice Research Centre, MARDI Seberang Perai
  • M.J. Masnan Institute of Engineering Mathematics, Universiti Malaysia Perlis


Rice Leaf Blast (RLB) Disease, Uncontrol Environment, Image Pre-processing, Colour Image Segmentation, Multi-level Image Thresholding,


Rice diseases have caused a major production and economic losses in the agricultural industry. To control and minimise the impacts of the attacks, the diseases need to be identified in the early stage. Early detection for estimation of severity effect or incidence of diseases can save the production from quantitative and qualitative losses, reduce the use of pesticide, and increase country’s economic growth. This paper describes an integrated method for detection of diseases on leaves called Rice Leaf Blast (RLB) using image processing technique. It includes the image pre-processing, image segmentation and image analysis where Hue Saturation Value (HSV) colour space is used. To extract the region of interest, image segmentation (the most critical task in image processing) is applied, and pattern recognition based on Multi-Level Thresholding approach is proposed. As a result, the severity of RLB disease is classified into three categories such as infection stage, spreading stage and worst stage.


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How to Cite

Abu Bakar, M., Abdullah, A., Abdul Rahim, N., Yazid, H., Misman, S., & Masnan, M. (2018). Rice Leaf Blast Disease Detection Using Multi-Level Colour Image Thresholding. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-15), 1–6. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4036