scholarly journals Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation

2018 ◽  
Vol 10 (9) ◽  
pp. 1402 ◽  
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.

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

2020 ◽  
Vol 7 (2) ◽  
pp. 191941
Author(s):  
Jian Yang ◽  
Lin Du ◽  
Wei Gong ◽  
Shuo Shi ◽  
Jia Sun
Keyword(s):  
Fluorescence Spectrum ◽  
Nitrogen Concentration ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Concentration ◽  
Growth Status ◽  
First Derivative ◽  
Fluorescence Characteristics ◽  
The Difference

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).

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

2019 ◽  
Vol 9 (5) ◽  
pp. 916 ◽  
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.

Variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen concentration

2014 ◽  
Vol 38 (6) ◽  
pp. 640-652 ◽  
Author(s):  
YAN Shuang ◽  
◽  
ZHANG Li ◽  
JING Yuan-Shu ◽  
HE Hong-Lin ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Concentration ◽  
The Relationship

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

2015 ◽  
Vol 7 (11) ◽  
pp. 14939-14966 ◽  
Author(s):  
Xia Yao ◽  
Yu Huang ◽  
Guiyan Shang ◽  
Chen Zhou ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Concentration ◽  
Wheat Leaf

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

2006 ◽  
Vol 86 (4) ◽  
pp. 1037-1046 ◽  
Author(s):  
Yan Zhu ◽  
Yingxue Li ◽  
Wei Feng ◽  
Yongchao Tian ◽  
Xia Yao ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Nutrition Status ◽  
Nitrogen Accumulation ◽  
Canopy Reflectance ◽  
Leaf Nitrogen Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

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