典型文献
Leaf recognition using BP?RBF hybrid neural network
文献摘要：
Plant recognition has great potential in forestry research and management. A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples. The process was carried out in three steps: image pretreatment, feature extraction, and leaf recognition. In the image pretreatment processing, an image segmentation method based on hue, saturation and value color space and connected component labeling was presented, which can obtain the complete leaf image without veins and back-ground. The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recogni-tion. The recognition accuracy of different classifiers was used to compare classification performance. The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2％, highest among all the classifiers.
文献关键词：
中图分类号：
作者姓名：
Xin Yang;Haiming Ni;Jingkui Li;Jialuo Lv;Hongbo Mu;Dawei Qi
作者机构：
College of Science,Northeast Forestry University, Harbin 150040,People's Republic of China
文献出处：
引用格式：
[1]Xin Yang;Haiming Ni;Jingkui Li;Jialuo Lv;Hongbo Mu;Dawei Qi-.Leaf recognition using BP?RBF hybrid neural network)[J].林业研究（英文版）,2022(02):579-589
A类：
B类：
Leaf,recognition,using,RBF,hybrid,neural,network,Plant,has,great,potential,forestry,research,management,new,method,combined,back,propagation,radial,basis,function,identify,tree,species,few,features,samples,was,carried,three,steps,image,pretreatment,extraction,leaf,In,processing,segmentation,hue,saturation,value,color,space,connected,component,labeling,presented,which,can,obtain,complete,without,veins,ground,used,test,influence,shape,texture,accuracy,different,classifiers,compare,classification,performance,nine,dimensional,highest,among,all
AB值：
0.577538
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