[1] Zhao Guowei. Measures of how to enhance the germination rate of rice seeds[J]. Private Technology, 2014, 17: 195.
[2] Shi Yaping, Cai Jingping. Study on rapid determination of seed germination rate[J]. Cereals and Oils Processing, 2008, 2: 13-14.
[3] Xiong Z, Sun D W, Dai Q, et al.. Application of visible hyperspectral imaging for prediction of springiness of fresh chicken meat[J]. Food Analytical Methods, 2015, 8(2): 380-391.
[4] Mahesh S, Jayas D S, Paliwal J, et al.. Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk samples of Canadian wheat[J]. Food and Bioprocess Technology, 2015, 8(1): 31-40.
[5] 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): 073003.
[6] Zhang Baohua, Huang Wenqian, Li Jiangbo, et al.. Detection of bruises and early decay in apples using hyperspectral imaging and PCA[J]. Infrared and Laser Engineering, 2013, 42(S2): 279-283.
[7] Zhang Yabiao, Luo Ju, Tang Jian, et al.. Hyperspectral characteristics of rice leaves and their pigment and water content analysis[J]. Journal of Anhui Agricultural Sciences, 2015, 43(7): 40-44.
[8] Kong W, Zhang C, Liu F, et al.. Rice seed cultivar identification using near- infrared hyperspectral imaging and multivariate data analysis[J]. Sensors, 2013, 13(7): 8916-8927.
[9] Huang Shuangping, Qi Long, Ma Xu, et al.. Grading method of rice panicle blast severity based on hyperspectral image [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(1): 212-219.
[10] Deng Xiaoqin, Zhu Qibing, Huang Min. Variety discrimination for single rice seed by integrating spectral, texture and morphological features based on hyperspectral image[J]. Laser & Optoelectronics Progress, 2015, 52(2): 021001.
[11] Li Meiling, Deng Fei, Liu Ying, et al.. Study on detection technology of rice seed vigor based on hyperspectral image[J]. Acta Agriculturae Zhejiangensis, 2015, 27(1): 1-6.
[14] Liu Minfa, Zhang Lingbiao, He Jianguo, et al.. Study on non-destructive detection of pesticide residues on Lingwu long jujubes’surface using hyperspectral imaging[J]. Food & Machinery, 2014, 5: 87-92.
[15] National Standardization Technical Committee. GB/T 3543.1~3543.7-1995 Rules for agricultural seed testing[S]. Beijing: China Standard Press, 1995.
[17] Xiong Z, Sun D W, Dai Q, et al.. Application of visible hyperspectral imaging for prediction of springiness of fresh chicken meat[J]. Food Analytical Methods, 2015, 8(2): 380-391.
[18] Yu Xiaoya, Zhang Yujun, Yin Gaofang, et al.. Feature wavelength selection of phytoplankton fluorescence spectra based on partial least squares[J]. Acta Optica Sinica, 2014, 34(9): 0930002.
[20] Sun Zhonghua, Wang Jue, Qu Zhong. Application of neural networks based on GA to identification of loading materials for initiating explosive device[J]. Microcomputer Development, 2003, 13(8): 3-5.
[21] Liu Zaiwen, Li Mengxun, Wang Xiaoyi, et al.. The method of mid- term and short- term prediction for water bloom based on LSSVM and RBFNN[J]. Computer and Applied Chemistry, 2012, 29(10): 1189-1194.
[22] Liu Boping, Qin Huajun, Luo Xiang, et al.. Multicomponent quantitative analysis using near infrared spectroscopy by building PLS-GRNN model[J]. Spectroscopy and Spectral Analysis, 2007, 27(11): 2216-2220.
[23] Wu Julan, Zhou Xiaomei, Fan Lingjuan, et al.. Effects of artificial aging on seed vigor, physiological and biochemical characteristics of soybean seeds[J]. Chinese Journal of Oil Crop Sciences, 2011, 33(6): 582-587.
[24] Cheng Hong, Shi Zhixing, Yin Huijuan, et al.. Detection of multi- corn kernel embryos characteristic using machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(19): 145-151.
[25] Cai Chunju, Fan Shaohui, Liu Feng, et al.. Physiological and biochemical changes of moso bamboo (Phyllostachys edulis) seeds in artificial aging[J]. Scientia Silvae Sinicae, 2013, 49(8): 29-34.
[26] Tan Meilian, Xiao Ningning, Yan Mingfang, et al.. Effects of sun flower artificial aging on seed vigor and physiological characteristics[J]. Journal of Anhui Agricultural Sciences, 2010, 38(23): 12319-12322.
[27] Fu Yifeng, Li Hongyan, Huang Fan, et al.. Physiological and seed vigor changes of Elymus sibiricus L. seeds during artificial aging[J]. Journal of Plant Genetic Resources, 2014, 15(6): 1360-1363.