基于CS-SVM的谷子叶片病害图像识别

利用图像识别方法对常见的谷子叶片病害进行判别,为制定合理的病害防治措施提供科学依据。试验采集了谷瘟病、白发病、红叶病、锈病共4种谷子叶片病害的原始图像,运用基于超绿特征的最大类间方差法对谷子叶片病害进行分割,提取谷子叶片病害颜色、形态、纹理等共计19个特征,采用蚁群优化算法选择了8个特征。运用布谷鸟算法(cuckoo search,CS)优化支持向量机(support vector machine,SVM)的惩罚因子c和径向核函数g,利用SVM对谷子叶片病害进行自动判别。结果表明,当c=80.2662,g=1.8467时,谷子叶片病害和叶片的平均识别率达到99%,表明基于CS-SVM的图像识别方法可对4种谷子叶片病害进行准确分类。

Image recognition of millet leaf disease based on CS-SVM

The method of image recognition was used to distinguish the common diseases of millet leaves and provide scientific basis for the development of reasonable disease control measures.The original images of four kinds of leaf diseases including millet blast disease, white hair disease, red leaf disease and rust diseasewere were collected.The maximum variance method based on super green characteristics was used to segment the leaf diseases of millet. A total of 19 features of millet leaf disease were extracted, including color, shape and texture. The experiment adopted ant colony optimization algorithm to select 8 features. Cuckoo search (CS) was used to optimize the penalty factor c and radial kernel function g of SVM, and SVM was used to automatically identify millet leaf diseases. When c=80.2662 and g=1.8467, the average identification rate of millet leaf disease and leaves was reached 99%. These results showed that the method of image recognition based on support vector machine was feasible for the classification of four millet leaf diseases.