基于可见光谱和支持向量机的黄瓜叶部病害识别方法研究

以黄瓜叶部病害作为研究对象，基于可见光谱反射率差异识别黄瓜叶部病害，研究基于SVM的黄瓜叶部病害识别预测模型。采用小波变换进行数据预处理；选取Otsu、边缘分割法和K均值聚类三类分割方法进行病斑分割，比较错分率和运行时间，K均值聚类方法更适合黄瓜叶部病斑分割；提取纹理、颜色和形状特征参数，共15个特征参数；通过交叉验证选择最优参数c和g，对核函数参数进行优化处理，并通过比较线性核、多项式核、RBF核等不同核函数情况下SVM的正确识别率，确定RBF核SVM模式识别方法能够更精准地识别黄瓜叶部病害。并将基于SVM与另外两种常见的黄瓜叶部病害识别方法，BP神经网络和模糊聚类进行比较，结果表明，基于SVM的识别模型对霜霉病的正确识别率为95%，白粉病和褐斑病的正确识别率均为90%，平均诊断正确率为92%；该模式识别方法识别效果最佳，运行时间最短，为基于可见光谱的黄瓜病害识别模型提供参考。

Recognition Method of Cucumber Leaves Diseases Based on Visual Spectrum and Support Vector Machine

In this paper, we used cucumber leaves disease as the research object, and identified cucumber leaves disease based on the difference of visible spectral reflectance. The support vector machine recognition is an efficient recognition method, which is always used as identification model. For cucumber leaves diseases, if we constructed the support vector machine based on digital image process, we can get accurate and efficient recognition. Consequently, this paper studied the cucumber leaves disease recognition method based on support vector machine. Firstly, the method of wavelet domain denoising was applied to image denoising. The segmentation results were compared with K mean clustering, OTSU and edge segmentation. The results showed that K-means clustering method was more accurate. We extracted texture, color and shape feature parameters, 15 feature parameters. Then, the optimal parameters of c and g were selected by cross-validation, and the parameters of the kernel function were optimized and using RBF kernel to construct SVM classifier. By comparing the linearity kernel, polynomial kernel and RBF kernel of the SVM recognition’s correct rate, we got that the RBF kernel is most accurate for the recognition of the cucumber leaf disease. Therefore, we used RBF kernel to construct SVM classifier. Finally, there was an identification model of cucumber leaf disease which was based on SVM classifier, and two other efficient identification models, back Propagation neural network, fuzzy clustering identification model. We constructed three kinds of identification models through comparing the correct recognition rate and running time. The results of the test showed that the cucumber downy mildew's correct recognition rate based on SVM classifier was 95%. The correct recognition rate of cucumber powdery mildew and brown spot was 90%, and the average diagnosis accuracy was 92%. In addition, the method running time was the shortest. In summary, the results show that, among the three recognition methods, cucumber leaves disease recognition based on the SVM classifier is the most suitable, demonstrating that the method can be used to rapidly identify cucumber leaves diagnosis based on visual spectrum.