激光与光电子学进展, 2015, 52 (4): 041102, 网络出版: 2015-04-02   

基于局部学习的玉米种子近红外高光谱图像鉴选 下载: 508次

Discrimination of Maize Seeds by Near Infrared Ray Hyperspectral Imaging with Local Learning
作者单位
江南大学轻工业过程先进控制教育部重点实验室, 江苏 无锡 214122
摘要
将局部学习算法引入到种子的近红外高光谱图像最优波段选择中,并建立偏最小二乘判别分析分类预测模型,实现少波段条件下的玉米种子的快速鉴选。实验共采集了6 类样本共720 粒的玉米种子在874~1734 nm 波段范围内的256 幅近红外高光谱图像,利用局部学习算法获得波段的特征权重,并依据特征权重选择了最优波段。实验结果表明局部学习算法可有效获取最优鉴选波段,在13 个最优波段条件下,对6 组玉米种子可以获得平均纯度为95.97%的鉴选结果,为实现玉米种子的快速鉴选提供了一个合适的技术途径。
Abstract
The local learning algorithm is introduced into the optimal wavelength selection of near infrared ray hyperspectral imaging of maize seeds. These obtained wavelengths are used to develop a discrimination model coupled with partial least squares discriminant analysis to implement the rapid discrimination of maize seeds using less wavelengths. 256 near infrared ray hyperspectral images between 874~1734 nm wavelengths are acquired using a hyperspectral imaging system for 720 maize seed samples including six varieties. Local learning algorithm is proposed to calculate the weight values of wavelengths, and the optimal wavelengths are selected according to the weight values. The experimental results show that local learning algorithm can effectively select the optimal wavelengths. Using 13 optimal wavelengths, six groups of maize seeds achieve an average purity of 95.97%, which can provide a suitable technical way for the rapid discrimination of maize seeds.
参考文献

[1] 成雪峰, 张凤云. 种子检验技术的现状与展望[J]. 种子, 2009, 28(8): 58-62.

    Cheng Xuefeng, Zhang Fengyun. Status and prospect of seed testing technology [J]. Seed, 2009, 28(8): 58-62.

[2] 孟庆宽, 何洁, 仇瑞承, 等. 基于机器视觉的自然环境下作物行识别与导航线提取[J]. 光学学报, 2014, 34(7): 0715002.

    Meng Qingkuan, He Jie, Qiu Ruicheng, et al.. Crop recognition and navigation line detection in natural environment based on machine vision [J]. Acta Optica Sinica, 2014, 34(7): 0715002.

[3] 刘燕德, 应义斌, 成芳, 等. 机器视觉技术在种子纯度检验中的应用[J]. 农业机械学报, 2003, 34(5): 161-163.

    Liu Yande, Ying Yibin, Cheng Fang, et al.. Research of machine vision in purity inspection of seed [J]. Transactions of the Chinese Society for Agricultural Machinery, 2003, 34(5): 161-163.

[4] 赵杰文, 毕夏坤, 林颢, 等. 鸡蛋新鲜度的可见—近红外透射光谱快速识别[J]. 激光与光电子学进展, 2013, 50(5):053003.

    Zhao Jiewen, Bi Xiakun, Lin Hao, et al.. Visible-near-infrafed transmission spectra for rapid analysis of the freshness of eggs [J]. Laser & Optoelectronics Progress, 2013, 50(5): 053003.

[5] 郭培源, 林岩, 付妍, 等. 基于近红外光谱技术的猪肉新鲜度等级研究[J]. 激光与光电子学进展, 2013, 50(3): 033002.

    Guo Peiyuan, Lin Yan, Fu Yan, et al.. Research on freshness level of meat based on near-infrared spectroscopic technique [J]. Laser & Optoelectronics Progress, 2013, 50(3): 033002.

[6] 张海东, 李贵荣, 李若诚, 等. 近红外光谱结合极限学习机和GA-PLS 算法检测普洱茶茶多酚含量[J]. 激光与光电子学进展, 2013, 50(4): 043001.

    Zhang Haidong, Li Guirong, Li Ruocheng, et al.. Determination of tea polyhenols content in puerh tea using near-infrared spectroscopy combined with extreme learning machine and GA-PLS algorithm [J]. Laser & OptoelectronicsPprogress, 2013, 50(4): 043001.

[7] 杨锦忠, 郝建平, 杜天庆, 等. 基于种子图像处理的大数目玉米品种形态识别[J]. 作物学报, 2008, 34(6): 1069-1073.

    Yang Jinzhong, Hao Jianping, Du Tianqing, et al.. Discrimination of numerous maize cultivars based on seed image process [J]. Acta Agronomica Sinica, 2008, 34(6): 1069-1073.

[8] 洪添胜, 乔军, 李震, 等. 基于高光谱图像技术的雪花梨品质无损检测[J]. 农业工程学报, 2007, 23(2): 151-155.

    Hong Tiansheng, Qiao Jun, Li Zhen, et al.. Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique [J]. Transactions of the CSAE, 2007, 23(2): 151-155.

[9] 薛龙, 黎静, 刘木华. 基于高光谱图像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.

[10] 赵杰文, 惠喆, 黄林, 等. 高光谱成像技术检测鸡肉中挥发性盐基氮含量[J]. 激光与光电子学进展, 2013, 50(7): 073007.

    Zhao Jiewen, Hui Zhe, Huang Lin, et al.. Quantitative detection of TVB-N content in chicken meat with hyperspectral imaging technology [J]. Laser & Optoelectronics Progress, 2013, 50(7): 073007.

[11] 冯朝丽, 朱启兵, 朱晓, 等. 基于光谱特征的玉米品种高光谱图像识别[J]. 江南大学学报(自然科学版), 2012, 11(2): 149-153.

    Feng Zhaoli, Zhu Qibing, Zhu Xiao, et al.. Maize variety recognition using hyperspectral Image [J]. Journal of Jiangnan University (Natural Science), 2012, 11(2): 149-153.

[12] 张初, 刘飞, 何勇, 等. 利用近红外高光谱图像技术快速鉴别西瓜种子品种[J]. 农业工程学报, 2013, 29(20): 270-277.

    Zhang Chu, Liu Fei, He Yong, et al.. Fast identification of watermelon seed variety using near infrared hyperspectral imaging technology [J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(20): 270-277.

[13] 朱启兵, 冯朝丽, 黄敏, 等. 基于高光谱图像技术和SVDD 的玉米种子识别[J]. 光谱学与光谱分析, 2013, 33(2): 517-521.

    Zhu Qibing, Feng Zhaoli, Huang Min, et al.. Maize seed identification using hyperspectral imaging and SVDD algorithm [J]. Spectroscopy and Spectral Analysis, 2013, 33(2): 517-521.

[14] 朱启兵, 冯朝丽, 黄敏, 等. 基于图像熵信息的玉米种子纯度高光谱图像识别[J]. 农业工程学报, 2012, 28(23): 271-276.

    Zhu Qibing, Feng Zhaoli, Huang Min, et al.. Maize seed classification based on image entropy using hyperspectral imaging technology [J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(23): 271-276.

[15] 王加华, 韩东海. 基于遗传算法的苹果糖度近红外光谱分析[J]. 光谱学与光谱分析, 2008, 28(10): 2308-2311.

    Wang Jiahua, Han Donghai. Analysis of near-infrared spectra of apple SSC by genetic algorithm optimization [J]. Spectroscopy and Spectral Analysis, 2008, 28(10): 2308-2311.

[16] 王爽, 黄敏, 朱启兵. 基于无信息变量和偏最小二乘投影分析的高光谱散射图像最优波段选择[J]. 光子学报, 2011, 40(3): 428-432.

    Wang Shuang, Huang Min, Zhu Qibing. Optimal wavelength selection of hyperspectral scattering images based on UVEPLS projection analysis [J]. Acta Photonica Sinica, 2011, 40(3): 428-432.

[17] Sun Y, Todorovic S, Goodison S. Local-learning-based feature selection for high-dimensional data analysis [J]. Pattern Analysis and Machine Intelligence, 2010, 32(9): 1610-1626.

[18] Macho S, Callao M P, Larrechi M S, et al.. Monitoring ethylene content in heterophasic copolymers by near-infrared spectroscopy: Standardisation of the calibration model [J]. Analytica Chimica Acta, 2001, 445(2): 213-220.

[19] 曹慧, 刘玉峰. 未标记样本在半监督学习中的应用方法研究[J]. 广西轻工业, 2008, (12): 80-82.

    Cao Hui, Liu Yufeng. Not marked sample research in the application of a semi-supervised learning method [J]. Guangxi Journal of Light Industry, 2008, (12): 80-82.

[20] Williams P, Norris K. Near-Infrared Technology in the Agricultural and Food Industries [M]. Saint Paul: American Association of Cereal Chemists, 1987.

唐金亚, 黄敏, 朱启兵. 基于局部学习的玉米种子近红外高光谱图像鉴选[J]. 激光与光电子学进展, 2015, 52(4): 041102. Tang Jinya, Huang Min, Zhu Qibing. Discrimination of Maize Seeds by Near Infrared Ray Hyperspectral Imaging with Local Learning[J]. Laser & Optoelectronics Progress, 2015, 52(4): 041102.

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