Research on Leaf Classification Algorithm Based on the Image

机译：基于图像的叶片分类算法研究

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The MATLAB image processing toolbox is applied to extract 8 classical features of leaf (including perimeter, area, roundness, complexity, elongation, sphericity, average coefficient variation, serration), and 400 leaf samples are classified respectively on BP Neural Network, Probabilistic Neural Network (PNN) and Support Vector Machine (SVM), and the coverage recognition rate for BP Neural Network, PNN and SVM are obtained as 87.22%, 88.95% and 95.15% respectively. The coverage recognition rate of SVM is the highest and stable, which can effectively prevent the low recognition rate.

机译：应用MATLAB图像处理工具箱以提取叶片的8个经典特征（包括周边，区域，圆度，复杂性，伸长率，球形，平均系数变异，锯齿）和400叶样品分别对BP神经网络，概率神经网络进行分类（PNN）和支持向量机（SVM），以及BP神经网络，PNN和SVM的覆盖识别率分别为87.22％，88.95％和95.15％。 SVM的覆盖识别率是最高且稳定的，可以有效地防止低识别率。

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《International Conference on Social Network, Communication and Education》|2018年|636p|共5页
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作者
Zhiliang Kang; Peng Huang;
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中图分类 TP393-53;
关键词
Leaf classification; Image processing; Neural network; Support vector machine (SVM);

机译：叶分类;图像处理;神经网络;支持向量机（SVM）;

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7. Research on Leaf Classification Algorithm Based on the Image [O] . Zhiliang Kang, Peng Huang 2018

机译：基于图像的叶片分类算法研究

Research on Leaf Classification Algorithm Based on the Image

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