光谱学与光谱分析 |
|
|
|
|
|
Testing of Germination Rate of Hybrid Rice Seeds Based on Near-Infrared Reflectance Spectroscopy |
LI Yi-nian, JIANG Dan, LIU Ying-ying, DING Wei-min*, DING Qi-shuo, ZHA Liang-yu |
Key Laboratory of Intelligent Equipment for Agriculture of Jiangsu Province College, School of Engineering, Nanjing Agricultural University,Nanjing 210031, China |
|
|
Abstract Germination rate of rice seeds was measured according to technical stipulation of germination testing for agricultural crop seeds at present. There existed many faults for this technical stipulation such as long experimental period, more costing and higher professional requirement. A rapid and non-invasive method was put forward to measure the germination rate of hybrid rice seeds based on near-infrared reflectance spectroscopy. Two varieties of hybrid rice seeds were aged artificially at temperature 45 ℃ and humidity 100% condition for 0,24,48,72,96,120 and 144 h. Spectral data of 280 samples for 2 varieties of hybrid rice seeds with different aging time were acquired individually by near-infrared spectra analyzer. Spectral data of 280 samples for 2 varieties of hybrid rice seeds were randomly divided into calibration set (168 samples) and prediction set (112 samples). Gormination rate of rice seed with different aging time was tested. Regression model was established by using partial least squares (PLS). The effect of the different spectral bands on the accuracy of models was analyzed and the effect of the different spectral preprocessing methods on the accuracy of models was also compared. Optimal model was achieved under the whole bands and by using standardization and orthogonal signal correction (OSC) preprocessing algorithms with CM2000 software for spectral data of 2 varieties of hybrid rice seeds, the coefficient of determination of the calibration set (RC) and that of the prediction set (RP) were 0.965 and 0.931 individually, standard error of calibration set (SEC) and that of prediction set (SEP) were 1.929 and 2.899 respectively. Relative error between tested value and predicted value for prediction set of rice seeds is below 4.2%. The experimental results show that it is feasible that rice germination rate is detected rapidly and nondestructively by using the near-infrared spectroscopy analysis technology.
|
Received: 2013-08-12
Accepted: 2013-12-21
|
|
Corresponding Authors:
DING Wei-min
E-mail: wmding@njau.edu.cn
|
|
[1] LIANG Liang, YANG Min-hua, LIU Zhi-xiao, et al(梁 亮, 杨敏华, 刘志霄, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009,29(11): 2962. [2] Li XL, He Y, Wu CQ. Journal of Stored Products Research, 2008, 44(3): 264. [3] HAN Liang-liang, MAO Pei-sheng, WANG Xin-guo, et al(韩亮亮, 毛培胜, 王新国, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 27(2): 86. [4] KANG Yue-qiong, HAO Feng(康月琼,郝 风). Seed(种子), 2004, 23(7): 10. [5] YIN Jia-hong, MAO Pei-sheng, HUANG Ying, et al(阴佳鸿, 毛培胜, 黄 莺, 等). Infrared(红外), 2010, 31(7): 39. [6] Olesen M H, Shetty N, Gislum R, et al. Journal of Near Infrared Spectroscopy, 2011, 19(3): 171. [7] Min Tai Gi, Kang Woo Sik. Korean Journal of Crop Science, 2008, 53(3): 314. [8] Min Tai Gi, Kang Woo Sik. Horticulture, Environment and Biotechnology, 2008, 49(1): 42. [9] ZHI Ju-zhen, BI Xin-hua, DU Ke-min, et al(支巨振,毕辛华,杜克敏,等). Rules for Agricultural Seed Testing-Germination Test(农作物种子检验规程-发芽试验), 1995. [10] YANG Ya-ping,JIANG Xiao-cheng,CHEN Liang-bi,et al(杨亚平,姜孝成,陈良碧,等). Journal of Hunan Agricultural University(Natural Sciences)(湖南农业大学学报·自然科学版), 2008, 34(3): 265. [11] LU Xin-xiong, CHEN Xiao-ling(卢新雄, 陈晓玲). Scientia Agricultura Sinica(中国农业科学), 2002, 35(8): 975. [12] FU Xia-ping, YING Yi bin, LIU Yan-de, et al(傅霞萍, 应义斌, 刘燕德, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(6): 1038. [13] LI Jie, ZHANG Xiao-chao, YUAN Yan-wei, et al(李 颉, 张小超, 苑严伟, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(2): 176.
|
[1] |
WANG Wen-xiu, PENG Yan-kun*, FANG Xiao-qian, BU Xiao-pu. Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2094-2100. |
[2] |
LE Ba Tuan1, 3, XIAO Dong1*, MAO Ya-chun2, SONG Liang2, HE Da-kuo1, LIU Shan-jun2. Coal Classification Based on Visible, Near-Infrared Spectroscopy and CNN-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2107-2112. |
[3] |
LIU Jin, LUAN Xiao-li*, LIU Fei. Near Infrared Spectroscopic Modelling of Sodium Content in Oil Sands Based on Lasso Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2274-2278. |
[4] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
[5] |
XU Wei-jie1, WU Zhong-chen1, 2*, ZHU Xiang-ping2, ZHANG Jiang1, LING Zong-cheng1, NI Yu-heng1, GUO Kai-chen1. Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1926-1932. |
[6] |
LI Ying1, LI Yao-xiang1*, LI Wen-bin2, JIANG Li-chun3. Model Optimization of Wood Property and Quality Tracing Based on Wavelet Transform and NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1384-1392. |
[7] |
DU Jian1, 2, HU Bing-liang1*, LIU Yong-zheng1, WEI Cui-yu1, ZHANG Geng1, TANG Xing-jia1. Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1514-1519. |
[8] |
HAN Guang, LIU Rong*, XU Ke-xin. Extraction of Effective Signal in Non-Invasive Blood Glucose Sensing with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1599-1604. |
[9] |
WANG Li-shuang, ZHANG Wen-bo*, TONG Li. Studies on Dimensional Stability of Wood under Different Moisture Conditions by Near Infrared Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1066-1069. |
[10] |
HUANG Hua1, WU Xi-yu2, ZHU Shi-ping1*. Feature Wavelength Selection and Efficiency Analysis for Paddy Moisture Content Prediction by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1070-1075. |
[11] |
LI Hao-guang1,2, YU Yun-hua1,2, PANG Yan1, SHEN Xue-feng1,2. Study of Maize Haploid Identification Based on Oil Content Detection with Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1089-1094. |
[12] |
PENG Cheng1, FENG Xu-ping2*, HE Yong2, ZHANG Chu2, ZHAO Yi-ying2, XU Jun-feng1. Discrimination of Transgenic Maize Containing the Cry1Ab/Cry2Aj and G10evo Genes Using Near Infrared Spectroscopy (NIR)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1095-1100. |
[13] |
LI Shuang-fang1,2, GUO Yu-bao1*, SUN Yan-hui2, GU Hai-yang2. Rapid Identification of Sunflower Seed Oil Quality by Three-Dimensional Synchronous Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1165-1170. |
[14] |
LU Wei, GUO Yang-ming, DAI De-jian, ZHANG Cheng-yu, WANG Xin-yu. Rice Germination Rate Detection Based on Fluorescent Spectrometry and Deep Belief Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1303-1312. |
[15] |
XIA Ji-an1, YANG Yu-wang1*, CAO Hong-xin2, HAN Chen1, GE Dao-kuo2, ZHANG Wen-yu2. Classification of Broad Bean Pest of Visible-Near Infrared Spectroscopy Based on Cloud Computing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 756-760. |
|
|
|
|