基于粒子群算法和支持向量机的黄花菜叶部病害识别  

Recognition of Hemerocallis citrina Leaf Disease Based on PSO and SVM

作　　者：孙瑜[1] 张永梅[1] 武玉军 SUN Yu;ZHANG Yongmei;WU Yujun(College of Information Science and Engineering,Shanxi Agriculture University,Taigu,Shanxi 030801;Datong University,Datong,Shanxi 037000)

机构地区：[1]山西农业大学信息科学与工程学院,山西太谷030801 [2]大同大学,山西大同037000

出　　处：《中国农学通报》2022年第8期135-140,共6页Chinese Agricultural Science Bulletin

基　　金：国家自然科学基金“数字图像处理技术辅助相场法模拟压电织构陶瓷晶粒取向生长”(52102138);山西省基础研究计划青年科学研究“基于机器视觉的动物个体及其姿势识别研究”(20210302124497)。

摘　　要：使用数字图像处理技术,以黄花菜叶部病害图像为识别对象,基于Lab空间和K-means聚类算法分割病害区域,提取目标区域的颜色特征、方向梯度直方图(histogram of oriented gradient,HOG)特征和形状特征,分别建立单一特征模型和特征融合模型,采用粒子群(particle swarm optimization,PSO)算法通过交叉验证优化支持向量机(support vector machine,SVM)模型的惩罚因子和核参数,建立基于PSOSVM的多特征融合分类模型识别黄花菜病害。基于SVM的多特征融合分类模型识别率高于单一特征分类模型,识别率可达为81.67%;基于PSO-SVM多特征融合分类模型识别率高达92.39%。基于PSOSVM的多特征分类模型识别率高,可以及时、便捷、高效地识别黄花菜病害。By using digital image processing technology,the disease image of Hemerocallis citrina leaf was taken as the recognition object.The disease area was segmented based on Lab space and K-means clustering algorithm,and the color characteristics,histogram of oriented gradient(HOG)and shape characteristics of the target areas were extracted from the images.The single-feature model and multi-feature model were established respectively based on the extracted features.Particle swarm optimization(PSO)algorithm was used to optimize the penalty factor and kernel parameter of the support vector machine(SVM)model through cross validation.Multi-feature classification model based on PSO-SVM was established to identify diseases of H.citrina leaves.The recognition rate of SVM based multi-feature classification model was higher than that of single-feature classification model,and the recognition rate could reach 81.67%.The recognition rate of multifeature classification model based on PSO-SVM was as high as 92.39%.The multi-feature classification model based on PSO-SVM has high recognition rate and can identify the disease of H.citrina leaf timely,conveniently and efficiently.

关 键 词：图像处理 黄花菜 病害识别 支持向量机 粒子群算法 多特征融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]