Research on Identification of Cucumber, Stem and Leaf Based on Spectrum Analysis Technology
WANG Hai-qing, JI Chang-ying*, CHEN Kun-jie
Key Laboratory of Intelligent Agricultural Equipment of Higher Education Institute in Jiangsu Province,College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Abstract:To be able to quickly identify the cucumber real time, the present paper studied the near infrared reflectance characteristics of cucumber, stem and leaf. Spectral reflectance of 138 samples (46 cucumbers, 46 stems and 46 leaves) was collected using near infrared spectroscopy in the band range of 600~1 099 nm indoor. After Savitzky-Golay smoothing preprocessing, random 108 spectral samples were put forward as calibration set. The weighted deviation method was used for choosing the spectral bands 690~950 nm that include much more information. The samples were analyzed by PCA method to extract the principal component scores, combining the Mahalanobis distance method the recognition model was established, and seven abnormal samples were excluded. The partial least squares (PLS) model was established by remaining 101 samples spectra of calibration set, which was used for predicting the validation set (30 samples except of the calibration set). The result shows that the correlation of the predicted value and the actual value reaches up to 0.994 1, and the correct recognition rate is 100%. This significantly illustrates that the near infrared spectral reflectance characteristics are different among the cucumbers, stems and leaves, which can be successfully applied to recognition of cucumber by the method. The developed technique can provide a new method for cucumber identification.
Key words:Spectral analysis;Cucumber recognition;Principal component analysis;Partial least squares;Mahalanobis distance
王海青,姬长英*,陈坤杰. 基于光谱分析技术的黄瓜与茎叶识别研究[J]. 光谱学与光谱分析, 2011, 31(10): 2834-2838.
WANG Hai-qing, JI Chang-ying*, CHEN Kun-jie . Research on Identification of Cucumber, Stem and Leaf Based on Spectrum Analysis Technology . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(10): 2834-2838.
[1] Yang qing-hua, Qi li-yong, Bao Guanjun, et al. New Zealand Journal of Agricultural Research, 2007, 50(5): 989. [2] YUAN Ting, ZHANG Jun-xiong, LI Wei, et al(袁 挺,张俊雄,李 伟,等). Transactions of the Chinese Society of Agricaltural Machinery(农业机械学报),2009, 40(8): 170. [3] Eui-Cheol Shin, Brian D. Craft, Ronald B. Pegg, et al. Food Chemistry, 2010, 119: 1262. [4] Abdulhamit Subasi, Ismail Gursoy M. Expert Systems with Applications, 2010, 37: 8661. [5] CHEN Bin, ZOU Xian-yong, ZHU Wen-jing(陈 斌,邹贤勇,朱文静). Journal of Jiangsu University·Natural Science Edidtion(江苏大学学报·自然科学版), 2008, 29(4): 278. [6] ZENG Jiu-sun, LIU Xiang-guan, LUO Shi-hua, et al(曾九孙,刘祥官,罗世华,等). Journal of Zhejiang University·Science Edition(浙江大学学报·理学版), 2009, 36(1): 34. [7] Delalieux S, van Aardt J, Keulemans W, et al. European Journal of Agronomy, 2007, 27: 130. [8] Jonesa C D, Jonesb J B, Leea W S. Computers and Electronics in Agriculture, 2010, 74: 329. [9] Ana M Aguilera, Manuel Escabias, Cristian Preda, et al. Chemometrics and Intelligent Laboratory Systems, 2010, 104: 289. [10] Efron B, Gong G. The American Statistician, 1983, 37(1): 44. [11] Eike Luedeling, Adam Hale, Minghua Zhang, et al. International Journal of Applied Earth Observation and Geoinformation, 2009, 11: 247.