基于改进支持向量机算法的农业害虫图像识别研究

Image recognition of agricultural pest based on improved support vector machine

为提高农业害虫图像识别效果,采用改进支持向量机算法。首先通过交叉验证优化惩罚因子,Manhattan距离确定核函数选择;然后建立农业害虫图像的特征模型,包括颜色特征、纹理特征、形状特征;接着对害虫图像多特征融合识别,各种害虫的颜色特征、纹理特征、形状特征所分配的权值通过Fisher计算,避免害虫识别误判的发生。试验仿真显示:害虫图像多特征平均识别率高于单一性特征、两特征,本文算法ISVM对害虫平均识别率均值为95.67%,相比NN、ACNN、FL、SVM、PPSVM分别提高9.81%、6.82%、5.57%、3.93%、1.90%,本文算法检测结果优于其他算法。

In order to improve the effect of agricultural pest image recognition,the improved support vector machine algorithm was proposed.Firstly,cross validation was optimized the penalty factor,and the Manhattan distance was used to determine the kernel function selection.Secondly,the feature model of agricultural pest image was established,including color feature,texture feature and shape feature.Thirdly,the multi feature fusion recognition of pest image was carried out,and the weight of color feature,texture feature and shape feature of various pests were calculated using Fisher,so that avoiding pest misrecognition.Experimental simulation showed that the average recognition rate of multi features pests were better than single feature and two features,average recognition rate of ISVM was 95.67%,it was 9.81%,6.82%,5.57%,3.93%and 1.90%higher than NN,ACNN,FL,SVM and PPSVM,detection result of ISVM was better than other algorithms.