| 基于PCA—SVM的棉花出苗期杂草类型识别 |
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| 引用本文： | 李慧,祁力钧,张建华,冀荣华.基于PCA—SVM的棉花出苗期杂草类型识别[J].农业机械学报,2012,43(9):184-189,196. |
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| 作者姓名： | 李慧 祁力钧 张建华 冀荣华 |
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| 作者单位： | 1. 中国农业大学工学院,北京,100083
2. 中国农业大学信息与电气工程学院,北京,100083 |
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| 基金项目： | 农业部行业科技专项资助项目(201203025);中国农业大学研究生科研创新专项资助项目（2012YJ262） |
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| 摘 要： | 为了实现棉田中不同类型杂草的机器视觉识别,提出基于主成分分析和支持向量机的棉花出苗期杂草识别方法。该方法通过提取棉田图像中棉花和杂草的颜色、形状、纹理等特征,并利用主成分分析(PCA)降低特征变量空间维数,结合支持向量机,实现对棉田杂草类型分类。通过120个棉花杂草测试样本分类试验结果发现,经PCA降维得到的前3个主成分分量能有效减少支持向量机的训练时间和提高分类正确率;通过对比发现前3个主成分分量与径向基核函数支持向量机相结合效果最好,其训练时间为91 ms,平均分类正确率达98.33%。
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| 关 键 词： | 棉花 杂草识别 图像处理 主成分分析 支持向量机 |
Recognition of Weed during Cotton Emergence Based on Principal Component Analysis and Support Vector Machine |
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| Li Hui,Qi Lijun,Zhang Jianhua and Ji Ronghua.Recognition of Weed during Cotton Emergence Based on Principal Component Analysis and Support Vector Machine[J].Transactions of the Chinese Society of Agricultural Machinery,2012,43(9):184-189,196. |
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| Authors: | Li Hui Qi Lijun Zhang Jianhua and Ji Ronghua |
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| Institution: | China Agricultural University;China Agricultural University;China Agricultural University;China Agricultural University |
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| Abstract: | A method of recognition weeds during cotton emergence based on principal component analysis (PCA) and support vector machine (SVM) was developed. For the effective classification of the variety of weeds in cotton field, the dimension of feature variable space was reduced by extracting color, shape, texture characteristics and principal component analysis. The experiment of classification for 120 samples of cottons and weeds showed that it was able to reduce training time and increase classification accuracy effectively by the first three principal components obtained by PCA dimensionality reduction. It was found by comparison that the best classification and recognition result was obtained by using the combination of the first three principal components and RBF kernel function SVM. The training time is 91ms and the average correct classification rate is 98.33%. |
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| Keywords: | Cotton Weed recognition Image processing Principal component analysis Support vector machine |
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