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.
作者
孙瑜
张永梅
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)
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)。
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
image processing
Hemerocallis citrina
disease recognition
support vector machine(SVM)
particle swarm optimization(PSO)
multi-feature fusion
