Multifrequency on-off modulation and slow light characterization of the patterned black phosphorus metamaterial based on dual plasmon-induced transparency.

We present a monolayer patterned black phosphorus (BP) metamaterial for generating a tunable dual plasmon-induced transparency (PIT). We have derived the expression for the theoretical transmittance by introducing the coupled mode theory (CMT), and the calculated results of the expression highly overlap with the simulation results. The quarterly frequency synchronous switch with two different operating bands is designed by the carrier density and scattering rate on the dual PIT modulation effect. Two parameters were selected as important markers to show the performance of the optical switch: the modulation depth (MD) and the insertion loss (IL). The theoretical analysis of this structure shows that the higher modulation depth (5.45d B

Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice.

Paddy rice is one of the most significant food sources and an important part of the ecosystem. Thus, accurate monitoring of paddy rice growth is highly necessary. Leaf nitrogen content (LNC) serves as a crucial indicator of growth status of paddy rice and determines the dose of nitrogen (N) fertilizer to be used. This study aims to compare the predictive ability of the fluorescence spectra excited by different excitation wavelengths (EWs) combined with traditional multivariate analysis algorithms, such as principal component analysis (PCA), back-propagation neural network (BPNN), and support vector machine (SVM), for estimating paddy rice LNC from the leaf level with three different fluorescence characteristics as input variables. Then, six estimation models were proposed. Compared with the five other models, PCA-BPNN was the most suitable model for the estimation of LNC by improving R2 and reducing RMSE and RE. For 355, 460 and 556 nm EWs, R2 was 0.89, 0.80 and 0.88, respectively. Experimental results demonstrated that the fluorescence spectra excited by 355 and 556 nm EWs were superior to those excited by 460 nm for the estimation of LNC with different models. BPNN algorithm combined with PCA may provide a helpful exploratory and predictive tool for fluorescence spectra excited by appropriate EW based on practical application requirements for monitoring the N status of crops.

Potential of spectral ratio indices derived from hyperspectral LiDAR and laser-induced chlorophyll fluorescence spectra on estimating rice leaf nitrogen contents.

Accurate estimation of leaf nitrogen contents (LNCs) is essential for nutrition management in monitoring crop growth status. The aim of this study was to compare the potential of hyperspectral LiDAR (HSL) and laser-induced chlorophyll fluorescence (LIF) data in accurately predicting rice LNC. First of all, the intensity values of HSL at 694 and 742 nm and LIF at ~685 and ~740 nm were selected as the characteristic variables to analyze rice LNC using data collected in 2014 and 2015, respectively. Second, spectral indices derived from HSL (only) and LIF (only) were utilized to estimate LNC of rice, respectively. Third, a combined ratio indices (the ratio indices of reflectance to fluorescence and NDVI-based indices at the above four wavelengths) was developed and evaluated in estimating rice LNC. The statistical method of linking these spectral indices to rice LNC was the artificial neural network, which was to obtain the optimum performance in LNC estimation of rice. The results demonstrated that the combined ratio indices, especially the ratio of reflectance to fluorescence at ~740 nm, showed a moderate relationship with rice LNC (R2 = 0.736, 0.704, and 0.713 for the 2014 first experiment, 2014 second experiment, and 2015 experiment, respectively).

Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra.

Paddy rice is one of the most important crops in China, and leaf nitrogen content (LNC) serves as a significant indictor for monitoring crop status. A reliable method is needed for precise and fast quantification of LNC. Laser-induced fluorescence (LIF) technology and reflectance spectra of crops are widely used to monitor leaf biochemical content. However, comparison between the fluorescence and reflectance spectra has been rarely investigated in the monitoring of LNC. In this study, the performance of the fluorescence and reflectance spectra for LNC estimation was discussed based on principal component analysis (PCA) and back-propagation neural network (BPNN). The combination of fluorescence and reflectance spectra was also proposed to monitor paddy rice LNC. The fluorescence and reflectance spectra exhibited a high degree of multi-collinearity. About 95.38%, and 97.76% of the total variance included in the spectra were efficiently extracted by using the first three PCs in PCA. The BPNN was implemented for LNC prediction based on new variables calculated using PCA. The experimental results demonstrated that the fluorescence spectra (R2 = 0.810, 0.804 for 2014 and 2015, respectively) are superior to the reflectance spectra (R2 = 0.721, 0.671 for 2014 and 2015, respectively) for estimating LNC based on the PCA-BPNN model. The proposed combination of fluorescence and reflectance spectra can greatly improve the accuracy of LNC estimation (R2 = 0.912, 0.890 for 2014 and 2015, respectively).

Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer.

Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014-2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

Analyzing the performance of fluorescence parameters in the monitoring of leaf nitrogen content of paddy rice.

Leaf nitrogen content (LNC) is a significant factor which can be utilized to monitor the status of paddy rice and it requires a reliable approach for fast and precise quantification. This investigation aims to quantitatively analyze the correlation between fluorescence parameters and LNC based on laser-induced fluorescence (LIF) technology. The fluorescence parameters exhibited a consistent positive linear correlation with LNC in different growing years (2014 and 2015) and different rice cultivars. The R(2) of the models varied from 0.6978 to 0.9045. Support vector machine (SVM) was then utilized to verify the feasibility of the fluorescence parameters for monitoring LNC. Comparison of the fluorescence parameters indicated that F740 is the most sensitive (the R(2) of linear regression analysis of the between predicted and measured values changed from 0.8475 to 0.9226, and REs ranged from 3.52% to 4.83%) to the changes in LNC among all fluorescence parameters. Experimental results demonstrated that fluorescence parameters based on LIF technology combined with SVM is a potential method for realizing real-time, non-destructive monitoring of paddy rice LNC, which can provide guidance for the decision-making of farmers in their N fertilization strategies.