光学学报, 2009, 29 (s2): 159, 网络出版: 2010-01-27   

基于支持向量机方法的水果表面农药污染分类研究

Classification of Pesticide Contamination on Fruit Surface by Using Support Vector Machine
作者单位
1 江西农业大学工学院, 江西 南昌 330045
2 华东交通大学机电学院, 江西 南昌 330013
摘要
以支持向量机(SVM)的算法为基础,建立了近红外光谱识别脐橙表面被农药污染的定性分析模型。168个脐橙实验样本被随机的分为两组,第一组为建模集包含112个,用来建立SVM分类预测模型;第二组为预测集包含56个,用来对建立的模型验证其准确性。实验结果,该方法对脐橙是否被农药污染的正确识别率为100%(二类分类),对被不同浓度农药污染脐橙的正确识别率为87.5%(多类分类)。
Abstract
Based on support vector machine (SVM), a qualitative analysis model of near-infrared (NIR) spectra is set up to recognize navel oranges which are contaminated with pesticide or not. Total 168 navel oranges are randomly divided into two sets. Set 1 consists of 112 samples as a calibration set for developing the SVM model and set 2 consists of 56 samples and it is used to verify the prediction power of the calibration models. The experimental results show that the correct rate of recognizing whether the samples are contaminated by pesticide or not is 100% (two-class classification) and the correct rate of recognizing the samples contaminated by different concentrations pesticide is 87.5% (multi-class classification).
参考文献

[1] 薛龙, 黎静, 刘木华. 基于高光谱图象技术的水果表面农药残留检测试验研究[J]. 光学学报, 2008, 28(12): 2277~2280

    Xue Long, Li Jing, Liu Muhua. Detecting pesticide residue on navel orange surface by using hyperspectral imaging[J]. Acta Optica Sinica, 2008, 28(12): 2277~2280

[2] 胡淑芬, 刘木华, 林怀蔚. 基于激光图像的水果表面农药残留检测试验研究[J]. 江西农业大学学报(自然科学版), 2006, 28(6): 872~876

    Hu Shufen, Liu Muhua, Lin Huaiwei. A study on detecting pesticide residuals on fruit surface by using laser imaging[J]. Acta Agriculturae Universitatis Jiangxiensis (Natural Sciences Edition), 2006, 28(6): 872~876

[3] 张鹏翔, 周小芳, 方炎. 两种激发波长下蔬菜水果的拉曼光谱对比研究[J]. 光散射学报, 2004, 16(2): 136~140

    Zhang Pengxiang, Zhou Xiaofang, Fang Yan. Raman spectra of vegetables and fruits at two excitation wavelengths[J]. Chinese J. Light Scattering, 2004, 16(2): 136~140

[4] 周小芳, 方炎, 张鹏翔. 水果表面残留农药拉曼光谱研究[J]. 光散射学报, 2004, 16(1): 11~14

    Zhou Xiaofang, Fang Yan, Zhang Pengxiang. Raman spectra of pesticides on the surface of fruits[J]. Chinese J. Light Scattering, 2004, 16(1): 11~14

[5] V. N. Vapnik. The Nature of Statistical Learning Theory[M ]. New York: Springer, 1995

[6] V. N. Vapnik. Statistical learning theory[M]. New York: Wiley, 1998

[7] 白鹏, 冀捐灶 等. 基于SVM的混合气体分布模式红外光谱在线识别方法[J]. 光谱学与光谱分析, 2008, 28(10): 2278~2281

    Bai Peng, Ji Juanzao et al.. Method of infrared spectrum on-line pattern recognition of mixed gas distribution based on SVM[J]. Spectroscopy and Spectral Analysis, 2008, 28(10): 2278~2281

[8] 安欣, 徐硕. 多因变量LS-SVM回归算法及其在近红外光谱定量分析中的应用[J]. 光谱学与光谱分析, 2009, 29(1): 127~130

    An Xin, Xu Shuo. Multiple dependent variables LS-SVM regression algorithm and its application in NIR spectral quantitative analysis[J]. Spectroscopy and Spectral Analysis, 2009, 29(1): 127~130

[9] 房晓颖, 张效义, 袁佳. 基于LS-SVM 的辐射源分类[J]. 通信技术, 2009, 42(3): 13~15

    Fang Xiaoying, Zhang Xiaoyi, Yuan Jia. Radiator classification based on LS-SVM[J]. Communications Technology, 2009, 42(3): 13~15

[10] 韩勇鹏. SVM方法及其在乳制品分类问题上的应用[J]. 安徽农业科学, 2009, 37(8): 3345~3346

    Han Yongpeng. Introduction of the SVM and its application in the dairy products classification[J]. J. Anhui Agri. Sci., 2009, 37(8): 3345~3346

[11] 吴芳, 王晓原, 付宇. 基于LS-SVM 的交通流时序数据补齐方法[J]. 计算机工程与应用, 2008, 44(29): 232~235

    Wu Fang, Wang Xiaoyuan, Fu Yu. Method for filling time series data of traffic flow based on LS-SVM[J]. Computer Engineering and Applications, 2008, 44(29): 232~235

[12] 刘美, 黄道平, 孙宗海. 基于PCA和SVM的丁苯橡胶的门尼粘度预测[J]. 计算机与应用化学, 2008, 25(11): 1317~1320

    Liu Mei, Huang Daoping, Sun Zonghai. Prediction from the mooney-viscosity of SBR based on PCA and LS-SVM [J]. Computers and Applied Chemistry, 2008, 25(11): 1317~1320

黎静, 薛龙, 刘木华, 王晓, 罗春生. 基于支持向量机方法的水果表面农药污染分类研究[J]. 光学学报, 2009, 29(s2): 159. Li Jing, Xue Long, Liu Muhua, Wang Xiao, Luo Chunsheng. Classification of Pesticide Contamination on Fruit Surface by Using Support Vector Machine[J]. Acta Optica Sinica, 2009, 29(s2): 159.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!