寒地水稻稻瘟病显微图像分类方法研究

Study on Microscopic Image Classification of Rice Blast in Cold Area

针对稻瘟病显微图像的特点,提出将SVM应用于水稻稻瘟病快速分类的方法,首先对稻瘟病显微图像作预处理,然后进行形状及纹理特征提取,最后使用SVM进行稻瘟病识别。结果表明,SVM适用于小样本的稻瘟病孢子显微图像分类问题,研究中采用4种核函数进行分类准确率对比试验,使用径向基核函数的SVM分类准确率最高为98.7%。

For the characteristics of rice blast microscopic images,a method of applying SVM to the rapid classification of rice blast in rice is proposed.Firstly,the rice blast microscopic images are preprocessed,then shape and texture features are extracted,and finally the extracted features are identified as whether they are rice blast using SVM.The results show that SVM is suitable for the classification problem of small sample rice plague spore microscopic images.Four kernel functions were used in the study for the classification accuracy comparison test,and the highest SVM classification accuracy of 98.7%was achieved using the radial basis kernel function.