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

[2] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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.