多颜色空间中玉米叶部病害图像图论分割方法

Image Segmentation Based on Graph Theory in Multi-color Space for Maize Leaf Disease

摘要:

为了提高农田自然背景下玉米叶部病害诊断精度，提出了一种多颜色空间下的玉米叶部病害的图论分割方法。该方法在不同的颜色空间中引入图论进行分割，分别在单一颜色空间下将玉米病害的分割问题转换为图的分割问题，再通过有效的融合方法对初始的分割结果进行信息融合。通过对玉米叶部病害图像的分割实验表明，该方法的分割效果较好。在多种颜色空间下进行玉米叶部病害的图论分割方法是可行的、有效的。

Abstract:

To improve the accuracy of machine vision based maize leaf disease segmentation under the background of farmland, a graph theory based approach to maize leaf disease segmentation in multi-color space was proposed. The graph theory was used in different color spaces and the maize leaf disease segmentation was then formulated as a graph segmentation problem in different color spaces. Furthermore, an effective fusion method was applied to update the initial segment result. Experiments on the maize leaf disease showed that the segmentation results were good. The results revealed that the proposed approach was feasible and effective.