Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.

Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE–ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE–ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

The objectives of this work were to compare the performance of two variable selection methods, namely, Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm combined with partial least squares regression (PLSR), multiple linear regression (MLR), and least squares-support vector machine (LS-SVM) for the measurement of soil nitrogen (N), organic carbon (OC), available phosphorous (P), and available potassium (K) using near-infrared (NIR) spectroscopy, and provide interpretation for the effective variables. A total of 198 soil samples collected from nine towns in Wencheng County, People's Republic of China, were scanned by a Fourier-type NIR spectrometer. The entire data set was split randomly into calibration set (62%), validation set (19%), and prediction set (19%). The calibration set was used to build calibration models with a number of spectral variables optimized by the validation set. The prediction set was used for independent prediction of the established models. The proposed combination of MC-UVE-LS-SVM achieved the optimal prediction performance compared with PLSR, LS-SVM, successive projections algorithm-MLR, and MC-UVE-PLSR. The values of coefficient of determination (R 2 ) and residual prediction deviation were 0.88, 0.89, 0.59, and 0.75, and 2.9, 3.1, 1.5, and 2.0 for N, OC, P and K, respectively. The results showed that MC-UVE was able to select important variables from the NIR spectra, and LS-SVM was preferable to linear PLSR and MLR for the prediction of soil properties in this work. Analysis of effective variables indicated that: some of the effective variables of N and OC were directly associated with their functional groups, whereas some impacted on the prediction accuracy by measuring soil moisture content. Soil P and K were found to be measurable with different levels of agreement, which was partly attributed to the covariation of moisture content and illite in soil.

Fano resonances in metamaterial are important due to their low-loss subradiant behavior that allows excitation of high quality (Q) factor resonances extending from the microwave to the optical bands. Fano resonances have recently showed their great potential in the areas of modulation, filtering, and sensing for their extremely narrow linewidths. However, the Fano resonances in a metamaterial system arise from the interaction of all that form the structure, limiting the tunability of the resonances. Besides, sensing trace analytes using Fano resonances are still challenging. In the present work, we demonstrate the excitation of Fano resonances in metamaterial consisting of a period array of two concentric double-split-ring resonators with symmetry breaking (position asymmetry and gaps asymmetry). The tunability and sensing of Fano resonances are both studied in detail. Introducing position asymmetry in the metamaterial leads to one Fano resonance located at 0.50 THz, while introducing gaps asymmetry results in two Fano resonances located at 0.35 THz and 0.50 THz. The transmittance, position, and linewidth of the three Fano resonances can be easily tuned by varying the asymmetry deviations. The Q factor and figure of merit (FoM) of Fano resonances with different asymmetry deviations are calculated for performance optimization. The Fano resonances having the highest FoM are used for the sensing of analytes at different refractive indices, and the Fano resonance performing the best in refractive index sensing is further applied to detect the analyte thickness. The results demonstrate that the tunable Fano resonances show tremendous potential in sensing applications, offering an approach to engineering highly efficient modulators and sensors.

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The use of pesticides will have an impact on food, organisms, and environment. Specifically, pesticide residues in food will damage human health. Because of its high permeability, low energy, high spectral resolution, and fingerprint characteristics, terahertz frequency-domain spectroscopy has been introduced into the determination of pesticides (imidacloprid, acetamiprid, and triadimefon) residues in food samples (glutinous rice flour, wheat flour, and corn flour) in our present study. These three pesticides exhibit their own absorption peaks in the region of 0.4–1.7 THz. For understanding the origins of these peaks, the experimental data are interpreted by using density functional theory calculations at the level of B3LYP/6-31G (d). It is found that these absorption peaks come from the intramolecular and intermolecular interactions. The absorption peaks of pesticides are still detectable in a mixture of pesticides and food samples when they reach a certain concentration. The results from chemometrics analysis show that quantitative detection of pesticides in food samples is feasible. The partial least squares regression models have high correlation coefficient (>0.99), low root-mean-square error of calibration (<1.5%), low root-mean-square error of cross-validation (<2.4%), and low root-mean-square error of prediction (<2.3%), indicating good quality of prediction for pesticides concentration. Our results prove that the terahertz frequency-domain spectrum combined with chemometrics can be used for the detection of pesticides in food samples.

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