Abstract:

【Objective】 Image segmentation is one of the difficult issues in crop disease automatic identification systems, in present research, algorithms based on threshold or based on clustering were mostly used. These methods are simple and easy of implementation, but the precision is low. In order to improve the segmentation precision of maize disease spots, Gauss-MRF model was introduced to segment maize disease spot images. 【Method】 The algorithm was implemented on VC6.0. Gauss-Markov random field model takes advantage of both image intensity and spatial information imposed by Gibbs smoothness prior about the pixel labels and thus can be used to effectively segment the noised images. Four kinds of corn disease images were segmented, and segmentation results were compared with algorithms based on threshold and Gauss model. 【Result】 The segmentation algorithm based on G-MRF model showed higher precision, and the segmentation results showed accurate and closed boundaries. The mean correct classification ratio of G-MRF model was up to 96.35% which was higher than algorithms based on threshold and Gauss model, respectively, by 3.75% and 4.03%. 【Conclusion】 The research shows that the segmentation algorithm based on G-MRF model is satisfactory to separate disease part from normal part of leaves. The mean correct classification ratio is 96.35%. It is effective in segmenting and processing maize disease spots images.

Key words: Gauss, Markov, maize disease spots, segmentation