Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative

Leaf nitrogen concentration (LNC) is a major indicator in the estimation of the crop growth status which has been diffusely applied in remote sensing. Thus, it is important to accurately obtain LNC by using passive or active technology. Laser-induced fluorescence can be applied to monitor LNC in crops through analysing the changing of fluorescence spectral information. Thus, the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for paddy rice (Yangliangyou 6 and Manly Indica) LNC estimation was discussed, and then the proposed FS + FDFS was used to monitor LNC by multivariate analysis. The results showed that the difference between FS ( R 2 = 0.781, s.d. = 0.078) and FDFS ( R 2 = 0.779, s.d. = 0.097) for LNC estimation by using the artificial neural network is not obvious. The proposed FS + FDFS can improved the accuracy of LNC estimation to some extent ( R 2 = 0.813, s.d. = 0.051). Then, principal component analysis was used in FS and FDFS, and extracted the main fluorescence characteristics. The results indicated that the proposed FS + FDFS exhibited higher robustness and stability for LNC estimation ( R 2 = 0.851, s.d. = 0.032) than that only using FS ( R 2 = 0.815, s.d. = 0.059) or FDFS ( R 2 = 0.801, s.d. = 0.065).

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Selection of the optimal bands of first-derivative fluorescence characteristics for leaf nitrogen concentration estimation

Applied Optics ◽

10.1364/ao.58.005720 ◽

2019 ◽

Vol 58 (21) ◽

pp. 5720 ◽

Cited By ~ 4

Author(s):

Jian Yang ◽

Yinjia Cheng ◽

Lin Du ◽

Wei Gong ◽

Shuo Shi ◽

...

Keyword(s):

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Leaf Nitrogen Concentration ◽

First Derivative ◽

Concentration Estimation ◽

Fluorescence Characteristics ◽

Selection Of

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Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation

Remote Sensing ◽

10.3390/rs10091402 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1402 ◽

Cited By ~ 5

Author(s):

Jian Yang ◽

Shalei Song ◽

Lin Du ◽

Shuo Shi ◽

Wei Gong ◽

...

Keyword(s):

Fluorescence Intensity ◽

Nitrogen Concentration ◽

Back Propagation ◽

Principal Component ◽

Back Propagation Neural Network ◽

Leaf Nitrogen ◽

Excitation Wavelength ◽

Excitation Light ◽

Leaf Nitrogen Concentration ◽

Fluorescence Characteristics

Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation.

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Potential of Fluorescence Index Derived from the Slope Characteristics of Laser-Induced Chlorophyll Fluorescence Spectrum for Rice Leaf Nitrogen Concentration Estimation

Applied Sciences ◽

10.3390/app9050916 ◽

2019 ◽

Vol 9 (5) ◽

pp. 916 ◽

Cited By ~ 2

Author(s):

Jian Yang ◽

Lin Du ◽

Shuo Shi ◽

Wei Gong ◽

Jia Sun ◽

...

Keyword(s):

Fluorescence Spectrum ◽

Crop Yields ◽

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Kernel Functions ◽

Fluorescence Index ◽

Leaf Nitrogen Concentration ◽

Fluorescence Characteristics ◽

Hidden Layer ◽

Bpnn Model

Leaf nitrogen concentration (LNC) is a major biochemical parameter for estimating photosynthetic efficiency and crop yields. Laser-induced fluorescence, which is a promising potential technology, has been widely used to estimate the growth status of crops with the help of multivariate analysis. In this study, a fluorescence index was proposed based on the slope characteristics of fluorescence spectrum and was used to estimate LNC. Then, the performance of different fluorescence characteristics (proposed fluorescence index, fluorescence ratios, and fluorescence characteristics calculated by principal component analysis (PCA)) for LNC estimation was analyzed based on back-propagation neural network (BPNN) model. The proposed fluorescence index exhibited more stability and reliability for LNC estimation than fluorescence ratios and characteristics calculated by PCA. In addition, the effect of different kernel functions and hidden layer sizes of BPNN model on the accuracy of LNC estimation was discussed for different fluorescence characteristics. The optimal train functions “trainrp,” “trainbr,” and “trainlm” were then selected with higher R2 and lower standard deviation (SD) values than those of other train functions. In addition, experimental results demonstrated that the hidden layer size has a smaller impact on the accuracy of LNC estimation than the kernel function of the BPNN model.

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Analyzing the performance of the first-derivative fluorescence spectrum for estimating leaf nitrogen concentration

Optics Express ◽

10.1364/oe.27.003978 ◽

2019 ◽

Vol 27 (4) ◽

pp. 3978 ◽

Cited By ~ 9

Author(s):

Jian Yang ◽

Lin Du ◽

Wei Gong ◽

Shuo Shi ◽

Jia Sun ◽

...

Keyword(s):

Fluorescence Spectrum ◽

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Leaf Nitrogen Concentration ◽

First Derivative

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Assessing different regression algorithms for paddy rice leaf nitrogen concentration estimations from the first-derivative fluorescence spectrum

Optics Express ◽

10.1364/oe.395478 ◽

2020 ◽

Vol 28 (13) ◽

pp. 18728 ◽

Cited By ~ 2

Author(s):

Jian Yang ◽

Lin Du ◽

Yinjia Cheng ◽

Shuo Shi ◽

Chengzhi Xiang ◽

...

Keyword(s):

Fluorescence Spectrum ◽

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Paddy Rice ◽

Leaf Nitrogen Concentration ◽

First Derivative ◽

Rice Leaf ◽

Regression Algorithms

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Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat

Remote Sensing ◽

10.3390/rs10081315 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1315 ◽

Cited By ~ 4

Author(s):

Min Jia ◽

Jie Zhu ◽

Chunchen Ma ◽

Luis Alonso ◽

Dong Li ◽

...

Keyword(s):

Chlorophyll Fluorescence ◽

Winter Wheat ◽

Field Experiments ◽

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Small Scale ◽

Leaf Mass Per Area ◽

Leaf Nitrogen Concentration ◽

Solid Foundation ◽

The Difference

Precise detection of leaf nitrogen concentration (LNC) is helpful for nutrient diagnosis and fertilization guidance in farm crops. Numerous researchers have estimated LNC with techniques based on reflectance spectra or active chlorophyll fluorescence, which have limitations of low accuracy or small scale in the field. Given the correlation between chlorophyll and nitrogen contents, the response of sun-induced chlorophyll fluorescence (SIF) to chlorophyll (Chl) content reported in a few papers suggests the feasibility of quantifying LNC using SIF. Few studies have investigated the difference and power of the upward and downward SIF components on monitoring LNC in winter wheat. We conducted two field experiments to evaluate the capacity of SIF to monitor the LNC of winter wheat during the entire growth season and compare the differences of the upward and downward SIF for LNC detection. A FluoWat leaf clip coupled with a ASD spectrometer was used to measure the upward and downward SIF under sunlight. It was found that three (↓FY687, ↑FY687/↑FY739, and ↓FY687/↓FY739) out of the six SIF yield (FY) indices examined were significantly correlated to the LNC (R2 = 0.6, 0.51, 0.75, respectively). The downward SIF yield indices exhibited better performance than the upward FY indices in monitoring the LNC with the ↓FY687/↓FY739 being the best FY index. Moreover, the LNC models based on the three SIF yield indices are insensitive to the chlorophyll content and the leaf mass per area (LMA). These findings suggest the downward SIF should not be neglected for monitoring crop LNC at the leaf scale, although it is more difficult to measure with current instruments. The downward SIF could play an increasingly important role in understanding of the SIF emission for LNC detection at different scales. These results could provide a solid foundation for elucidating the mechanism of SIF for LNC estimation at the canopy scale.

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