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The β-glucan in oat grain plays an important role in preventing cardiovascular disease. Therefore, it is of great significance to realize the rapid measurement of β-glucan content in oat grain for the rational use of β-glucan. In this study, hyperspectral monitoring models of oat grain β-glucan content were constructed, and the effects of transformation algorithm, sensitive band screening algorithm, and modeling algorithm on the accuracy of the model were studied. The results showed that logarithm(Lg) and standardized normal variables(SNV) transformation can change the value of original spectral reflectance, while first-order differentiation(FD) and second-order differentiation(SD) transformation can also change the changing trend of original spectral reflectance. multivariate scattering correction(MSC), SNV, FD, and SD transformation can improve the correlation between spectral reflectance and oat grain β-glucan content to varying degrees. When using full band spectrum to construct the model, the performance of linear modeling algorithm was better than that of nonlinear modeling algorithm. Compared with uninformative variable elimination(UVE) algorithm, successive projections algorithm(SPA) had better band screening effect. Combining all the modeling results, it was found that MSC and SNV transformation algorithms had good results. Among them, the model based on SNV transformation spectrum and SPA-multiple linear regression(SPA-MLR) reached the highest accuracy, which can realize the hyperspectral monitoring of oat grain β-glucan content. [ABSTRACT FROM AUTHOR]

Tartary buckwheat is a common functional food. Its grains are rich in flavonoids and phenols. The rapid measurement of flavonoids and phenols in buckwheat grains is of great significance in promoting the development of the buckwheat industry. This study, based on multiple scattering correction (MSC), standardized normal variate (SNV), reciprocal logarithm (Lg), first-order derivative (FD), second-order derivative (SD), and fractional-order derivative (FOD) preprocessing spectra, constructed hyperspectral monitoring models of total flavonoids content and total phenols content in tartary buckwheat grains. The results showed that SNV, Lg, FD, SD, and FOD preprocessing had different effects on the original spectral reflectance and that FOD can also reflect the change process from the original spectrum to the integer-order derivative spectrum. Compared with the original spectrum, MSC, SNV, Lg, FD, and SD transformation spectra can improve the correlation between spectral data and total flavonoids and total phenols in varying degrees, while the correlation between FOD spectra of different orders and total flavonoids and total phenols in grains was different. The monitoring models of total flavonoids and total phenols in grains based on MSC, SNV, Lg, FD, and SD transformation spectra achieved the best accuracy under SD and FD transformation, respectively. Therefore, this study further constructed monitoring models of total flavonoids and total phenols content in grains based on the FOD spectrum and achieved the best accuracy under 1.6 and 0.6 order derivative preprocessing, respectively. The R2c, RMSEc, R2v, RMSEv, and RPD were 0.8731, 0.1332, 0.8384, 0.1448, and 2.4475 for the total flavonoids model, and 0.8296, 0.2025, 0.6535, 0.1740, and 1.6713 for the total phenols model. The model can realize the rapid measurement of total flavonoids content and total phenols content in tartary buckwheat grains, respectively. [ABSTRACT FROM AUTHOR]

The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R2c, RMSEc, R2v, RMSEv, and RPD are 0.9487, 0.1663 kg·m−2, 0.7335, 0.3600 kg·m−2, and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology. [ABSTRACT FROM AUTHOR]

Soil moisture controls the exchange of energy between the land surface and the atmosphere and significantly affects plant growth and productivity. Hyperspectral (350–2500 nm) monitoring of soil moisture content (SMC) could provide a theoretical basis for the real‐time estimation of spatial and temporal variations in soil moisture. During this study, we have developed SMC prediction models in different soil moisture ranges by using the combination of spectral preprocessing methods and optimized spectral indices. Results indicated that first‐order derivative (FD) preprocessing method can highlight the effective information of the spectra and improve the correlation between spectral reflectance and SMC. The spectral reflectance exhibited a strong correlation with SMC near 1400, 1700, 1900, and 2200 nm. The FD‐NDSI‐W0‐model had the best SMC prediction and applicability (R2 = 0.977, root mean square error = 3.413%, relative percent deviation = 6.171) to predict SMCs. Overall, pretreatment methods, combined with the two‐band random combination to optimize the spectral index to build a prediction model of SMC, could be applied for accurate monitoring of SMC. Core Ideas: The spectral features were enhanced using preprocessing methods.The characteristic bands of soil moisture content are clarified.The spectral indices were optimized using a two‐band random combination. [ABSTRACT FROM AUTHOR]

Purpose: Rapid and accurate estimation of soil organic carbon (SOC) based on near-infrared spectroscopy can assist sustainable agricultural developments in the future. Methods: In this study, based on the acquisition of soil spectra and SOC in field plot trials and farmland regional trials, the raw spectral data were processed by multi-class mathematical transformation to study the relationship between the raw spectra and their pre-processed spectra and SOC. Additionally, a near-infrared spectroscopy model has been constructed for the estimation of SOC based on multiple statistical regression (partial least squares, PLS; Support vector machine, SVM; successive projection algorithm-multiple linear regression, SPA-MLR) to resolve the optimal spectral pretreatment and modeling method suitable for SOC determination by Vis–NIR spectroscopy. Results: The results revealed that the soil NIR spectra were negatively correlated with SOC, which suggested that the spectral reflectance decreased gradually with the increasing SOC content. A comparative analysis of the correlation between the original and pre-treatment spectra and SOC further revealed that except for the Savitzky-Golay smoothing treatment (T1), different spectral pretreatments significantly improved the correlation between soil spectra and SOC, and the maximum correlation coefficient could reach up to 0.8. Among the three types of multiple regression models based on different pretreatment conditions, the support vector machine and model of soil organic carbon based on Savitzky-Golay smoothing + first derivative (T7) achieved better estimation results (R2c = 0.9212, RMSEc = 1.4058, RPDc = 4.3403; R2v = 0.7556, RMSEv = 2.4589, RPDv = 2.0186). Conclusions: Our findings provided some theoretical reference and practical application of the near-infrared spectroscopy-based estimation of organic carbon in agricultural soils. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this study is to realize the rapid and non-destructive determination of soil urease activity, so as to provide guidance for soil nitrogen transformation in time. Methods: In this study, five gradient experiments of water regulation were set up under the conditions of multiple cropping of winter wheat and summer soybean. The data of soil urease activity and hyperspectral reflectance were collected. We explored the influence of water regulation on soil urease activity. And based on a variety of spectral transformation algorithms and modeling algorithms, hyperspectral monitoring models of soil urease activity were constructed. Results: Soil urease activity increased first and then decreased with the aggravation of drought stress. FD, CR, MSC, and SNV transformation can improve the correlation between spectral reflectance and soil urease activity. The accuracy of the models constructed by PLSR and SMLR was high. In the nonlinear algorithm, SPA-ANN based on SNV had the highest accuracy. Among all the models, the PLSR model based on FD had the highest accuracy, with R2v of 0.8564, RMSEv of 0.4013, and RPD of 2.5667. This study can provide technical support for the rapid determination of soil urease activity and provide a theoretical basis for further rational management of farmland. [ABSTRACT FROM AUTHOR]

To extract the sensitive bands for estimating the winter wheat growth status and yields, field experiments were conducted. The crop variables including aboveground biomass (AGB), soil and plant analyzer development (SPAD) value, yield, and canopy spectra were determined. Statistical methods of correlation analysis, partial least squares (PLS), and stepwise multiple linear regression (SMLR) were used to extract sensitive bands and estimate the crop variables with calibration set. The predictive model based on the selected bands was tested with validation set. The results showed that the crop variables were significantly correlated with spectral reflectance. The major spectral regions were selected with the B-coefficient and variable importance on projection (VIP) parameter derived from the PLS analysis. The calibrated SMLR model based on the selected wavelengths demonstrated an excellent performance as the R2, TC, and RMSE were 0.634, 0.055, and 843.392 for yield; 0.671, 0.017, and 1.798 for SPAD; and 0.760, 0.081, and 1.164 for AGB. These models also performed accurately and robustly by using the field validation data set. It indicated that these wavelengths retained in models were important. The determined wavelengths for yield, SPAD, and AGB were 350, 410, 730, 1015, 1185 and 1245 nm; 355, 400, 515, 705, 935, 1090, and 1365 nm; and 470, 570, 895, 1170, 1285, and 1355 nm, respectively. This study illustrated that it was feasible to predict the crop variables by using the multivariate method. The step-by-step procedure to select the significant bands and optimize the prediction model of crop variables may serve as a valuable approach. The findings of this study may provide a theoretical and practical reference for rapidly and accurately monitoring the crop growth status and predicting the yield of winter wheat. [ABSTRACT FROM AUTHOR]

• The more fitting points, the smaller the fractional-order derivative spectrum. • Fractional-order derivative can improve the correlation with total nitrogen content. • Uninformative variable elimination can reduce the data dimension. • The accuracy of the model constructed with sensitive bands was better. • The number of fitting points, order, and bands of the best model are 40, 0.67 and 272. Realizing the real-time nondestructive monitoring of soil total nitrogen (STN) content is an important task to promote precision agriculture development. In this study, a water regulation experiment was performed. Winter wheat and summer soybean soils were taken as the research objects. The effects of fractional-order derivatives (FOD) on soil spectral reflectance were studied, and the hyperspectral monitoring models of STN content were constructed based on the full spectrum band and the uninformative variable elimination (UVE) -extracted band. The main results were as follows: The FOD showed a gradual change in the integer-order derivative (IOD). When calculating the FOD, the more fitting points there were, the smaller the FOD spectrum, and the higher the correlation with the STN content. In the range of orders 0–1, the correlation increased with increasing order. In the range of orders 1–2, the correlation first decreased and then increased. When constructing the model, stepwise multiple linear regression (SMLR) was used, but the accuracies of the SMLR models were low. Afterward, the bands were extracted by UVE, and the models were constructed by partial least squares regression (PLSR). The number of bands extracted by UVE ranged from 11 to 534, mainly in the range of 1000–2450 nm. Under the same conditions, the accuracy of the PLSR model constructed with the UVE-extracted bands was higher than that of the model constructed with the full band spectrum. Among all PLSR models, when the number of fitting points, the order, and the number of bands were 40, 0.67, and 272, respectively, the model accuracy was the highest. The coefficient of determination of cross-validation (R2 cv), root mean square error of cross-validation (RMSE cv), coefficient of determination of validation (R2 v), root mean square error of validation (RMSE v), and relative analysis error of validation (RPD v) of the model were 0.7317, 0.2244 g·kg−1, 0.7937, 0.1976 g·kg−1, and 2.1904, respectively. This model could realize the hyperspectral monitoring of STN content in wheat and soybean fields. In this study, hyperspectral spectroscopy was used to monitor the STN content, which provided a theoretical basis and technical support for the observations. [ABSTRACT FROM AUTHOR]