光谱学与光谱分析 |
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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 |
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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.
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Received: 2011-11-28
Accepted: 2012-03-13
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Corresponding Authors:
HAN Lie-bao
E-mail: hanliebao@163.com
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[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.
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