| 光谱学与光谱分析 |
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| Application of Hyperspectral Imaging for Visualization of Nitrogen Content in Pepper Leaf with Different Positions |
| YU Ke-qiang, ZHAO Yan-ru, LI Xiao-li, DING Xi-bin, ZHUANG Zai-chun, HE Yong* |
| College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
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Abstract In order to estimate pepper plant growth rapidly and accurately, hyperspectral imaging technology combined with chemometrics methods were employed to realize visualization of nitrogen content (NC) distribution. First, pepper leaves were picked up with the leaf number based on different leaf positions, and hyperspectraldata of these leaves were acquired. Then, SPAD and NC value of leaves were measured, respectively. After acquirement of pepper leaves’ spectral information, random-frog (RF) algorithm was chosen to extract characteristic wavelengths. Finally, five characteristic wavelengths were selected respectively, and then thosecharacteristic wavelengths and full spectra were used to establish partial least squares regression (PLSR) models, respectively. As a result, SPAD predicted model had an excellent performance of RC=0.970, RCV=0.965, RP=0.934, meanwhile evaluation parameters of NC predicted model were RC=0.857, RCV=0.806, RP=0.839. Lastly, according to the optimal models, SPAD and NC of each pixel in hyperspectral images of pepper leaves were calculated and their distribution was mapped. In fact, SPAD in plant can reflectthe NC. In this research, the change trend of both was similar, so the conclusions of this research were proved to be corrected. The results revealed that it was feasible to apply hyperspectral imaging technology for mapping SPAD and NC inpepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.
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Received: 2014-01-18
Accepted: 2014-04-11
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Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
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[1] Nguyen H T, Lee B W. European Journal of Agronomy,2006, 24(4): 349.
[2] WU Wei-hua(武维华). Plant physiology (2nd ed.)(植物生理学·第2版). Beijing：Sciences Press((北京：科学出版社), 2003.
[3] CHEN Zhi-qiang, WANG Lei, BAI You-lu, et al(陈志强，王 磊，白由路，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(4): 1066.
[4] QIU Zheng-jun, SONG Hai-yan, HE Yong, et al(裘正军，宋海燕，何 勇，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(7): 150.
[5] LI Gang-hua, XUE Li-hong, YOU Juan, et al(李刚华，薛利红，尤 娟，等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(6): 1127.
[6] ZHU Jin-xia, DENG Jin-song, LIN Fen-fang, et al(祝锦霞，邓劲松，林芬芳，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(4): 179.
[7] Zhang X L, Liu F, He Y, et al. Biosystems Engineering, 2013, 115(1): 56.
[8] Zou X B, Shi J Y, Hao L M, et al. Analytica Chimica Acta, 2011, 706(1): 105.
[9] Shi J Y, Zou X B, Zhao J W, et al. Scientia Horticulturae, 2012, 138(1): 190.
[10] Zhao C J, Wang J H, Huang W Q, et al. Precision Agriculture, 2010, 11: 557.
[11] Wu D, Nie P C, Cuello J, et al. Journal of Food Engineering, 2011, 102(3): 278.
[12] Li H D, Xu Q S, Liang Y Z. Analytica Chimica Acta, 2012, 740: 20.
[13] TANG Xiu-ying, NIU Li-zhao, XU Yang, et al(汤修映，牛力钊，徐 杨，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(11): 248.
[14] Macho S, Rius A, Callao, M P, et al. Analytica Chimica Acta, 2001, 445: 213. |
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