Automated rice leaf disease detection using color image analysis

摘要：

In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based Kmeans clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

关键词： rice histogram intersection K-means clustering outlier threshold brown spot leaf scald

作者: Reinald Adrian DL. Pugoy Vladimir Y. Mariano

作者单位: Institute of Computer Science, University of the Philippines Los Bafios,College, Laguna, Philippines 4031

会议类型: 国际会议

会议名称: Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)

会议地点: 成都

会议语种:英文

页码: 77-83

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）