| 基于支持向量机的水稻纹枯病识别研究 |
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| 引用本文： | 刘婷婷.基于支持向量机的水稻纹枯病识别研究[J].安徽农业科学,2011,39(28):17580-17582,17732. |
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| 作者姓名： | 刘婷婷 |
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| 作者单位： | 中国农业科学院蜜蜂研究所,北京,100093 |
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| 基金项目： | 国家863项目,“大田作物智能诊断技术系统研究与应用”(2007AA10Z237) |
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| 摘 要： | 目的]研究支持向量机对纹枯病病害进行自动识别,弥补人工识别的缺陷和不足,提高识别的准确性和效率。方法]以水稻纹枯病为研究对象,使用基于矢量中值滤波的方法对水稻纹枯病图像进行预处理。利用模糊C均值聚类法,在图像分割阶段进行灰度图像分割;分别从颜色、纹理和形状3个方面提取代表病斑的特征参数。最后用支持向量机识别方法进行水稻纹枯病识别,并与基于BP神经网络的识别方法进行对比。结果]识别率达到95.00%,要优于BP神经网络的91.88%。结论]基于支持向量机的水稻纹枯病识别弥补了人工识别的缺陷,也提高了准确性和效率,有广阔的应用前景。
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| 关 键 词： | 水稻纹枯病 支持向量机 特征提取 分类识别 |
Research on Recognition of Rhizocotonia solani Based on Support Vector Machine |
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| LIU Ting-ting.Research on Recognition of Rhizocotonia solani Based on Support Vector Machine[J].Journal of Anhui Agricultural Sciences,2011,39(28):17580-17582,17732. |
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| Authors: | LIU Ting-ting |
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| Institution: | LIU Ting-ting(School of Software and Microelectronics,Beijing University,Beijing 100093) |
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| Abstract: | Objective]The study aimed to research the automatic recognition of Rhizocotonia Solani by support vector machine(SVM) so as to make up the defect of artificial recognition and increase the accuracy and efficiency of recognition.Method]With R.Solani as the studied object,firstly,the method based on the vector median filtering was used to pre-treat the image of R.Solani,then the fuzzy c-mean clustering method was used to make for the gray image segmentation in the image segmentation stage and the feature pa... |
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| Keywords: | Rhizocotonia solani Support Vector Machine(SVM) Feature extraction Classification and recognition |
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