Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status J., Du L., Gong W., Sun J., Shi S., Biwu C. (2017): Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status. Plant Soil Environ., 63: 396-401.
download PDF
In this research, laser-induced fluorescence (LIF) technique combined with back-propagation neural network (BPNN) was employed to analyse different nitrogen (N) fertilization levels in paddy rice. Leaf fluorescence characteristics (FLCs) were measured by using the LIF system built in our laboratory and exhibited different FLCs with different nitrogen fertilization levels. The correlation between fluorescence intensity ratios (F685/F460, F735/F460 and F735/F685) and the dose of N fertilization was established and analysed. Then, the BPNN algorithm was utilized to validate that the different N fertilization levels can be classified based on the three FLCs. The overall identification accuracies of 2014 and 2015 were 90% and 92.5%, respectively. Experimental results demonstrated that the three FLCs with the help of multivariate analysis can be served as a helpful tool in the evaluation of paddy rice N fertilization levels. Besides, this study can also provide guidance for the selection of LIF Lidar channels in the following research.
Chappelle E.W., McMurtrey J.E., Wood F.M., Newcomb W.W. (1984a): Laser-induced fluorescence of green plants. 2: LIF caused by nutrient deficiencies in corn. Applied Optics, 23: 139–142.
Chappelle E.W., Wood F.M., McMurtrey J.E., Newcomb W.W. (1984b): Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation. Applied Optics, 23: 134–138.
Chappelle Emmett W., Wood Frank M., Wayne Newcomb W., McMurtrey James E. (1985): Laser-induced fluorescence of green plants 3: LIF spectral signatures of five major plant types. Applied Optics, 24, 74-
Wei Gong, Shalei Song, Bo Zhu, Shuo Shi, Faquan Li, Xuewu Cheng (2012): Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 1-9
H�k R., Lichtenthaler H. K., Rinderle U. (1990): Decrease of the chlorophyll fluorescence ratio F690/F730 during greening and development of leaves. Radiation and Environmental Biophysics, 29, 329-336
Hoge F. E., Swift R. N. (1981): Airborne simultaneous spectroscopic detection of laser-induced water Raman backscatter and fluorescence from chlorophyll a and other naturally occurring pigments. Applied Optics, 20, 3197-
Huang Xuehui, Kurata Nori, Wei Xinghua, Wang Zi-Xuan, Wang Ahong, Zhao Qiang, Zhao Yan, Liu Kunyan, Lu Hengyun, Li Wenjun, Guo Yunli, Lu Yiqi, Zhou Congcong, Fan Danlin, Weng Qijun, Zhu Chuanrang, Huang Tao, Zhang Lei, Wang Yongchun, Feng Lei, Furuumi Hiroyasu, Kubo Takahiko, Miyabayashi Toshie, Yuan Xiaoping, Xu Qun, Dong Guojun, Zhan Qilin, Li Canyang, Fujiyama Asao, Toyoda Atsushi, Lu Tingting, Feng Qi, Qian Qian, Li Jiayang, Han Bin (2012): A map of rice genome variation reveals the origin of cultivated rice. Nature, 490, 497-501
Li Wang, Sun Gang, Niu Zheng, Gao Shuai, Qiao Hailang (2014): Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system. Remote Sensing Letters, 5, 693-702
Lichtenthaler Hartmut K., Buschmann Claus (1987): Chlorophyll Fluorescence Spectra of Green Bean Leaves. Journal of Plant Physiology, 129, 137-147
McMurtrey J.E, Chappelle E.W, Kim M.S, Meisinger J.J, Corp L.A (1994): Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment, 47, 36-44
Pedrós Roberto, Moya Ismael, Goulas Yves, Jacquemoud Stéphane (2008): Chlorophyll fluorescence emission spectrum inside a leaf. Photochemical & Photobiological Sciences, 7, 498-
Schweiger Joachim, Lang Michael, Lichtenthaler Hartmut K. (1996): Differences in Fluorescence Excitation Spectra of Leaves between Stressed and Non-Stressed Plants. Journal of Plant Physiology, 148, 536-547
Subhash N., Mohanan C.N. (1994): Laser-induced red chlorophyll fluorescence signatures as nutrient stress indicator in Rice Plants. Remote Sensing of Environment, 47, 45-50
Tian Yong-Chao, Gu Kai-Jian, Chu Xu, Yao Xia, Cao Wei-Xing, Zhu Yan (2014): Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and Soil, 376, 193-209
Wu Chaoyang, Niu Zheng, Tang Quan, Huang Wenjiang (2008): Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148, 1230-1241
Yang J., Shi S., Gong W., Du L., YY Ma, Zhu B., SL Song (2016): Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content. Plant, Soil and Environment, 61, 182-188
Yang Jian, Du Lin, Sun Jia, Zhang Zhenbing, Chen Biwu, Shi Shuo, Gong Wei, Song Shalei (2016): Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra. Optics Express, 24, 19354-
Yang Jian, Sun Jia, Du Lin, Chen Biwu, Zhang Zhenbing, Shi Shuo, Gong Wei (2017): Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice. Optics Express, 25, 3743-
Yao Xia, Zhu Yan, Tian YongChao, Feng Wei, Cao WeiXing (2010): Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. International Journal of Applied Earth Observation and Geoinformation, 12, 89-100
Živčák M., Brestič M., Olšovská K. (2008): Assessment of physiological parameters useful in screening for tolerance to soil drought in winter wheat (Triticum aestivum L.) genotypes. Cereal Research Communications, 36: 1943–1946.
download PDF

© 2019 Czech Academy of Agricultural Sciences