Image-based Pretreatment Study of Rice Blast Disease


  • Zhiwei Shi Graduate School of Advanced Technology and Science Tokushima University, Japan. School of Mechanical Engineering Nantong University, China.
  • Stephen Karungaru Graduate School of Advanced Technology and Science Tokushima University, Japan.
  • Terada Kenji Graduate School of Advanced Technology and Science Tokushima University, Japan.
  • Hongjun Ni School of Mechanical Engineering Nantong University, China.
  • Shuaishuai Lv School of Mechanical Engineering Nantong University, China.
  • Xingxing Wang School of Mechanical Engineering Nantong University, China.
  • Yu Zhu School of Mechanical Engineering Nantong University, China.
  • Yi Lu School of Mechanical Engineering Nantong University, China.


Rice blight, Image pre-processing, Median filtering, Edge Segmentation, Feature Extraction


Rice blight has a great impact on rice yield and can lead to yield reduction of up to 70% in severe cases. Traditional detection methods require professional technicians to operate and are costly and inefficient, and cannot detect rice diseases in real-time. In this paper, we applied image detection technology to study rice blast disease based on the Matlab platform. Firstly, a basic rice blast database is built, and then a discussion is made on how to effectively improve the recognition success rate of rice blast images by two aspects: image pre-processing and feature extraction. The main research contents are as follows. (1) After studying the existing plant disease database, a basic rice blast database was constructed by field photography and other means. (2) Preprocessing of the collected rice blight images. Using the algorithm of rgb2gray function in Matlab, the images were grayed out; based on this, median filtering was used for noise reduction; then histogram equalization technique was used for image enhancement to increase the contrast and make the images clear; finally, various segmentation algorithms were used for image segmentation. (3) For the pre-processed rice blight images, feature extraction was performed in terms of the color of the disease to pave the way for feature selection.


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How to Cite

Zhiwei Shi, Stephen Karungaru, Terada Kenji, Hongjun Ni, Shuaishuai Lv, Xingxing Wang, Yu Zhu, & Yi Lu. (2024). Image-based Pretreatment Study of Rice Blast Disease. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). Retrieved from