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

2020 ◽  
Vol 28 (13) ◽  
pp. 18728 ◽  
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

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

2019 ◽  
Vol 27 (4) ◽  
pp. 3978 ◽  
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

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.

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