文章深入探讨了图像增强,基于病斑颜色与外轮廓相结合的病斑分割,有效特征提取,以及分类器构建等相关技术.并以五种容易混淆的病害为例,提取其病斑的色调、纹理、形态三种特征向量,分别采用支持向量机和BP神经网络进行训练、测试.实验结果表明该方法能很好的识别柠檬病害类别,为科学防治和病害危害程度评价提供科学依据.%The paper proposed an identification method through the analysis of disease image, the effective feature is extracted anto mattcall, and the classifier model is designed. In the paper method was studied how to enhancement processing the diseases of image, seg-mentat disease spot, extract feature,and Construct classifier model,etc. Then for example five of confusion between the diseases, extracting the disease spots the tone and texture, shape characteristics, after optimization respectively by using support vector machine (SVM) and BP neural network to identify disease categories. The experimental results show that this method can be a very good recognition plant disease categories for scientific control and give a scientific evaluation for the plant disease harm degree.
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