Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine J., Gong W., Shi S., Du L., Sun J., Ma Y.-., Song S.-. (2015): Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine. Plant Soil Environ., 61: 501-506.
download PDF

To identify accurately the doses of nitrogen (N) fertilizer and improve the photosynthetic efficiency of paddy rice, laser induced fluorescence (LIF) technique combined with the support vector machine (SVM) and principal component analysis (PCA) is proposed in this paper. The LIF technology, in which the ultraviolet light (355 nm) is applied as an excitation light source, is employed to measure fluorescence spectra of paddy rice. These fluorescence spectra demonstrate that the fluorescence spectral characteristics of paddy rice leaves with different doses of N fertilizer have distinct differences from each other. Then, PCA and SVM are implemented to extract the features of fluorescence spectra and to recognize different doses of N fertilizer, respectively. The overall recognition accuracy can reach 95%. The results show that the LIF technology combined with PCA and SVM is a convenient, rapid, and sensitive diagnostic method for detecting N levels of paddy rice. Thus, it will also be convenient for farmers to manage accurately their fertilization strategies.

Blackburn George Alan (1998): Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales. Remote Sensing of Environment, 66, 273-285
Brestic Marian, Zivcak Marek, Kalaji Hazem M., Carpentier Robert, Allakhverdiev Suleyman I. (2012): Photosystem II thermostability in situ: Environmentally induced acclimation and genotype-specific reactions in Triticum aestivum L. Plant Physiology and Biochemistry, 57, 93-105
Daughtry C (): Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74, 229-239
Günther K.P, Dahn H.-G, Lüdeker W (1994): Remote sensing vegetation status by laser-induced fluorescence. Remote Sensing of Environment, 47, 10-17
Gitelson Anatoly A, Buschmann Claus, Lichtenthaler Hartmut K (1999): The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants. Remote Sensing of Environment, 69, 296-302
Scott Green D., Erickson John E., Kruger Eric L. (2003): Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation. Agricultural and Forest Meteorology, 115, 163-171
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
Lichtenthaler H.K. (1987): Chlorophyll and carotenoids – Pigments of photosynthetic bio-membranes. Methods in Enzymology, 148: 350–382.
Li Fei, Mistele Bodo, Hu Yuncai, Chen Xinping, Schmidhalter Urs (2014): Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. European Journal of Agronomy, 52, 198-209
Li G.S., Zhang H., Wang Z.Q., Liu L.J., Yang J.C. (2007): Effects of nitrogen levels on grain yield and quality of rice. Journal of Yangzhou University, Agricultural and Life Science Edition, 28: 66–70.
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
Saito Yasunori, Kanoh Mitsuyoshi, Hatake Ken-ichiro, Kawahara Takuya D., Nomura Akio (): Investigation of Laser-Induced Fluorescence of Several Natural Leaves for Application to Lidar Vegetation Monitoring. Applied Optics, 37, 431-
Stroppiana Daniela, Boschetti Mirco, Brivio Pietro Alessandro, Bocchi Stefano (2009): Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111, 119-129
Stelzer Ernst H K (2014): Light-sheet fluorescence microscopy for quantitative biology. Nature Methods, 12, 23-26
Sherson Jacob F., Weitenberg Christof, Endres Manuel, Cheneau Marc, Bloch Immanuel, Kuhr Stefan (): Single-atom-resolved fluorescence imaging of an atomic Mott insulator. Nature, 467, 68-72
Song Shalei, Gong Wei, Zhu Bo, Huang Xin (2011): Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 672-682
Yang J., Shi S., Gong W., Du L., Ma Y.Y., Zhu B., Song S.L. (): Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content. Plant, Soil and Environment, 61, 182-188
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.
Živčák M., Olšovská K., Slamka P., Galambošová J., Rataj V., Shao H.B., Brestič M. (2014): Application of chlorophyll fluorescence performance indices to assess the wheat photosynthetic functions influenced by nitrogen deficiency. Plant, Soil and Environment, 60: 210–215.
download PDF

© 2020 Czech Academy of Agricultural Sciences