摘要
为保障小花清风藤的药材质量,建立基于HOG特征与BP神经网络相结合的小花清风藤叶片智能鉴别模型。采用方向梯度直方图(HOG)特征提取算子提取小花清风藤与簇花清风藤叶片的HOG特征,结合BP神经网络理论构建基于HOG特征的小花清风藤叶片智能鉴别神经网络模型。结果表明:该模型对小花清风藤叶片的平均识别率为97.60%,簇花清风藤为98.61%,对任意样本为100%。建立的基于HOG特征与BP神经网络相结合的小花清风藤叶片智能鉴别模型适合于小花清风藤叶片鉴别。
In order to ensure the quality of the medicinal materials of Sabia parviflora,an intelligent identification model of S.parviflora was established based on integrated HOG characteristics and BP neural network.The HOG features of the leaves of S.parviflora and Sabia swinhoei were extracted by using the direction gradient histogram(HOG)feature extraction operator.Integrated with BP neural network theory,the intelligent neural network model of S.parviflora was constructed based on HOG features.Results:The average recognition rate of the model was 97.60%for the leaves of S.parviflora,98.61%for that of S.swinhoei,and 100%for any sample.The established intelligent identification model of S.parviflora based on the combination of HOG features and BP neural network is suitable for the identification of S.parviflora leaves.
作者
郭文凯
孙庆文
GUO Wenkai;SUN Qingwen(Guizhou University of Traditional Chinese Medicine,Guiyang,Guizhou 550025,China)
出处
《贵州农业科学》
CAS
2019年第4期104-107,共4页
Guizhou Agricultural Sciences
基金
国家自然科学基金项目"基于神经网络和物元分析的民族药小花清风藤综合鉴定智能模型研究"(81560707)
贵州省国内一流学科建设项目"喀斯特地区特色药材引种驯化
野生抚育与品质评价研究"[GNYL(2017)008]
贵州省普通高等学校工程研究中心项目"贵州省中药民族药材种质资源保存及评价过程研究中心"[黔教合KY字(2017)018]
贵州省2015年度千层次创新型人才项目"贵州中药民族药资源研究"[贵中医(ZQ20150002)]
贵州省国内一流建设学科项目子课题"基于神经网络的小花清风藤叶片鉴别模型研究"[GNYL(2017)008号-7-Y]