基于FA-SVM技术的烟草早期病害识别

The Recognition of Early Tobacco Disease Based on FA-SVM Technology

为准确检测和识别烟草病害,为制定合理的病害防治措施提供科学依据,提出基于萤火虫算法优化支持向量机(FA-SVM)技术的烟草早期病害识别方法。以烟草常见的蛙眼病与赤星病为研究对象,利用可见光拍摄带有2种病害的烟草植物叶片,获取图像样本。利用形态学方法和图像分割技术得到病斑图像。提取病斑的颜色、纹理及形态学等共计32个特征,构建原始特征空间。利用蚁群算法(Ant colony optimization,ACO)对特征空间进行优化,依据适应度值选取最优特征组合,当适应度值达到最高为95.68时,有13个特征被选择。运用萤火虫算法(Firefly algorithm,FA)优化支持向量机(Support vector machine,SVM)的惩罚因子(c)与径向基核函数参数(g),提高分类器性能。当c=94.12、g=2.43时,对不同发育时期的2种病害的识别率达到96%。结果表明,利用FA-SVM技术识别烟草蛙眼病与赤星病2种常见病害是可行的。

For the accurate identification of tobacco disease and providing scientific basis for formulating control methods.A recognition method based on(FA-SVM)technology was proposed.The tobacco brown spot and frog eye disease were selected as the research object.The leaves of tobacco plants with two diseases were photographed by visible light.The segmentation and morphology methods were used to acquire disease spot images.Thirty-two features of spot were extracted to construct the original feature space,including color features,morphological features and texture features.The ant colony optimization(ACO)algorithm was used to extract the partial features build of the optimal feature space by the fitness function.The thirteen features were determined and the max fitness value was 95.68.The firefly algorithm(FA)was used to optimize the penalty factor(c)and the kernel function parameter(g)of support vector machine(SVM).The recognition accuracy of the classification model reached 96%when c=94.12,g=2.43.The results showed that the identification of tobacco disease is feasible based on FA-SVM technology.