| 光谱学与光谱分析 |
|
|
|
|
|
| Study on Varieties Identification of Kentucky Bluegrass Using Hyperspectral Imaging and Discriminant Analysis |
| XIAO Bo1，2, MAO Wen-hua3, LIANG Xiao-hong1, ZHANG Li-juan1, HAN Lie-bao1，2* |
1. Institute of Turfgrass Science, Beijing Forestry University, Beijing 100083, China
2. College of Gardening and Horticulture, Yangtze University, Jingzhou 434023, China
3. State Key Laboratory of Soil Plant Machinery System Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China |
|
|
|
|
Abstract Hyperspectral images of six varieties of Kentucky bluegrass were acquired using hyperspectral imager (550-1 000 nm) and the leaf spectral properties were extracted. Wilks’ lambda stepwise method was used and 9 optimal wavelengths were selected from the original 94 wavelengths and the discriminant models for varieties identification of Kentucky bluegrass were built based on Fisher’s linear discriminant function. The results showed that the Fisher’s linear discriminant model with 9 wavelengths achieved classification accuracies of 100% for both training and testing samples. While for the models with three wavelengths and six wavelengths, classification accuracies reached 83.3% and 96.7% for the testing samples, respectively. It indicates that hyperspectral images combined with discriminant analysis might be a good method to identify the varieties of Kentucky bluegrass.
|
|
Received: 2011-11-28
Accepted: 2012-03-13
|
|
|
|
Corresponding Authors:
HAN Lie-bao
E-mail: hanliebao@163.com
|
|
[1] Bonos S A, Murphy J A, Meyer W A, et al. Rutgers Turfgrass Proc., 2003, 34: 57.
[2] Bonos S A, Dickson W K, Park B S, et al. Rutgers Turfgrass Proc., 2004, 35: 45.
[3] Shortell R R, Dickson W K, Park B S, et al. Rutgers Turfgrass Proc., 2005, 36: 49.
[4] Murphy J A, Bonos S A, Perdomo P. Int. Turfgrass Soc. Res. J., 1997, 8: 1176.
[5] Cozzolino D, Fassio A, Restaino E, et al. Journal of Agricultural and Food Chemistry, 2008, 56(1): 79.
[6] Esteban-Diez I, Gonzalez-Saiz J M, Pizarro C. Analytica Chimica Acta, 2004, 514(1): 57.
[7] LIANG Liang, LIU Zhi-xiao, YANG Min-hua, et al(梁 亮，刘志霄，杨敏华，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(5): 353.
[8] LI Yang-peng, LI Wei-jun, LAI Jiang-liang(李阳鹏，李卫军，来疆亮). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(1): 119.
[9] ZOU Wei, FANG Hui, ZHOU Kang-yun, et al(邹 伟，方 慧，周康韵，等). Journal of Zhejiang University, Agriculture and Life Science Edition(浙江大学学报·农业与生命科学版), 2011, 37(2): 175.
[10] CAO Fang, WU Di, HE Yong, et al(曹 芳，吴 迪，何 勇，等) . Acta Optica Sinica(光学学报), 2009, 29(2): 537.
[11] JIANG Ke-sheng, KONG Li-chun, YU Peng, et al(姜科声，孔黎春，余 鹏，等). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2008, 25(4): 550.
[12] Lopez M. Near Infrared Spectroscopy, Proceedings of the 10th International Conference, Chichester, UK: NIR Publications, 2002. 335.
[13] JIANG Yi, ZENG Li-bo, WU Qiong-shui, et al(江 益，曾立波，吴琼水，等). Optical Technique(光学技术), 2005, 31(2): 193.
[14] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍，袁洪福，徐广通，等). Modern Near Infrared Spect roscopy Analytical Technology(Second Edition) (现代近红外光谱分析技术，第2版). Beijing : China Petrochemical Press(北京: 中国石油化工出版社), 2007.
|
| [1] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [2] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [4] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [5] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [6] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [7] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [8] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
| [9] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
| [10] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
| [11] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
| [12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
| [13] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
| [14] |
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702. |
| [15] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
|
|
|
|
