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Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance. The original spectral reflectance curves were preprocessed using 3 FOD methods: Grünwald-Letnikov (GL), Riemann-Liouville (RL), and Caputo. Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). The main results were as follows: For the 3 types of FOD, GL-FOD was suitable for analyzing the change process of the original spectral curve towards the integer-order derivative spectral curve. RL-FOD was suitable for constructing the hyperspectral monitoring model of winter wheat ChD. Caputo-FOD was not suitable for hyperspectral research due to its insensitivity to changes in order. The 3 FOD calculation methods could all improve the correlation between the original spectral curve and Log(ChD) to varying degrees, but only the GL method and RL method could observe the change process of correlation with order changes, and the shorter the wavelength, the smaller the order, and the higher the correlation. The bands screened by CARS were distributed throughout the entire spectral range, but there was a relatively concentrated distribution in the visible light region. Among all models, CARS was used to screen bands based on the 0.3-order RL-FOD spectrum, and the model constructed using ETsR reached the best accuracy and stability. Its R2c, RMSEc, R2v, RMSEv, and RPD were 1.0000, 0.0000, 0.8667, 0.1732, and 2.6660, respectively. In conclusion, based on the winter wheat ChD data set and the corresponding canopy hyperspectral data set, combined with 3 FOD calculation methods, 1 band screening method, and 8 modeling algorithms, this study constructed hyperspectral monitoring models for winter wheat ChD. The results showed that based on the 0.3-order RL-FOD, combined with the CARS screening band, ETsR modeling has the highest accuracy, and hyperspectral estimation of winter wheat ChD can be realized. The results of this study can provide some reference for the rapid and nondestructive estimation of ChD in winter wheat.

Better soil structure promotes extension of plant roots thereby improving plant growth and yield. Differences in soil structure can be determined by changes in the three phases of soil, which in turn affect soil function and fertility levels. To compare the quality of soil structure under different conditions, we used Generalized Soil Structure Index (GSSI) as an indicator to determine the relationship between the “input” of soil three phases and the “output” of soil structure. To achieve optimum monitoring of comprehensive indicators, we used Successive Projections Algorithm (SPA) for differential processing based on 0.0–2.0 fractional orders and 3.0–10.0 integer orders and select important wavelengths to process soil spectral data. In addition, we also applied multivariate regression learning models including Gaussian Process Regression (GPR) and Artificial Neural Network (ANN), exploring potential capabilities of hyperspectral in predicting GSSI. The results showed that spectral reflection, mainly contributed by long-wave near-infrared radiation had an inverse relationship with GSSI values. The wavelengths between 404-418 nm and 2193–2400 nm were important GSSI wavelengths in fractional differential spectroscopy data, while those ranging from 543 to 999 nm were important GSSI wavelengths in integer differential spectroscopy data. Also, non-linear models were more accurate than linear models. In addition, wide neural networks were best suited for establishing fractional-order differentiation and second-order differentiation models, while fine Gaussian support vector machines were best suited for establishing first-order differentiation models. In terms of preprocessing, a differential order of 0.9 was found as the best choice. From the results, we propose that when constructing optimal prediction models, it is necessary to consider indicators, differential orders, and model adaptability. Above all, this study provided a new method for an in-depth analyses of generalized soil structure. This also fills the gap limiting the detection of soil three phases structural characteristics and their dynamic changes and provides a technical references for quantitative and rapid evaluation of soil structure, function, and quality.

In order to find new potential fungicides and anticancer agents, a series of new coumarin amide derivatives bearing fluorine were synthesized and characterized spectroscopically. Compounds A6, B11, C2 and C7 were confirmed by X-ray diffraction further. The antifungal bioassays against five typical pathogenic fungi showed that compound C5 exhibited more remarkable fungicidal activities against Alternaria alternata (EC50 = 11.5 μg/mL), Colletotrichum gloeosporioides (EC50 = 18.0 μg/mL), Pyricularia grisea (EC50 = 33.8 μg/mL), surpassing kresoxim-methyl. Molecular docking result indicated that C5 displayed high binding affinity to chitinase, which plays crucial role in degradation and remodeling of fungal cell walls. In addition, the anticancer bioassays against three cancer cells demonstrated that compound A4 displayed excellent growth inhibitory effect against Hela cells with IC50 value of 8.13 μM, and low cytotoxicity against human normal cells BEAS-2B. Flow cytometric analysis further demonstrated that A4 significantly arrested cell cycle at the S phase and trigger apoptosis. With the above interesting biological profile, these coumarin derivatives could be used as anfungicides and anticancer candidates.

Urinary tract infections (UTIs), primarily caused by uropathogenic Escherichia coli (UPEC), have high morbidity and recurrence rates. Resistance to levofloxacin hydrochloride (LEV), a commonly used treatment for UTIs, is increasingly problematic, exacerbated by biofilm formation mediated by interactions between cyclic di-GMP (c-di-GMP or CDG) and YcgR. In this study, we identified three caffeoylquinic acid compounds from Pyrrosia lingua—chlorogenic acid (CGA), sibiricose A5 (Si-A5), and 3-O-caffeoylquinic acid methyl ester (CAM)—that target YcgR through molecular docking. Biological assays revealed that combining these compounds with levofloxacin hydrochloride significantly enhanced antibacterial activity against standard UPEC strains in a concentration-dependent manner and clinically isolated UPEC strains. Notably, chlorogenic acid and sibiricose A5, when used with levofloxacin hydrochloride, enhanced intracellular c-di-GMP levels and swimming motility, significantly reduced YcgR gene expression, and effectively inhibited biofilm formation of UPEC at multiple time points. Additionally, molecular dynamics simulations elucidated the strong binding of these compounds to YcgR, underscoring the critical roles of residues, such as Arg118 and Asp145. This research serves as a foundation for tackling antibiotic resistance and developing innovative therapeutics for UTIs.

Abstract Background Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed using a non-destructive approach. For this purpose, spectral data and protein content of grain and grain powder were obtained. After combining six types of preprocessed spectra and four types of multivariate statistical models, a relationship between hyperspectral datasets and grain protein is presented. Results It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of R v 2 = 0.814, RMSEv = 0.024 g/g, and RPDv = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of R v 2 = 0.789, RMSEv = 0.026 g/g, and RPDv = 2.177. Conclusions Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat. Graphical Abstract

To discover novel and effective potential anticancer agents, a series of azophenol derivatives containing 1,3,4-oxadiazoles moiety was synthesized and investigated for their anticancer activities against several human cancer cell lines by MTT method. Their structures were characterized by 1H NMR, 13C NMR, IR and HRMS spectral analyses. Among the prepared compounds, 5df displayed significant anti-proliferative activity against HCT116 cancer cells with an IC50 value of 4.09 ± 0.04 µM. Moreover, this compound had low cytotoxicity against normal cells. Flow cytometric analysis indicated that compound 5df arrested the cell cycle at S phase and induced apoptosis in a dose-dependent manner. Additionally, network pharmacology analysis calculated that 5df might target several key proteins, including AKT serine/threonine kinase 1 (AKT1), SRC proto-oncogene, non-receptor tyrosine kinase (SRC). Furthermore, molecular docking study indicated that 5df exhibited potentially high binding affinity to these target proteins with binding energies lower than −8 kcal/mol. These findings provide valuable insights for the development of azophenol derivatives as potential anticancer agents.

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices.

Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield prediction. This study utilizes a multi-sensor-equipped unmanned aerial vehicle (UAV) to collect remote sensing data during critical growth stages of millet, including spectral, textural, thermal, and point cloud information. The use of RGB point cloud data facilitated plant height extraction, enabling subsequent analysis to discern correlations between spectral parameters, textural indices, canopy temperatures, plant height, and biomass. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models were constructed to evaluate the capability of different features and integrated multi-source features in estimating the AGB. Findings demonstrated a strong correlation between the plant height derived from point cloud data and the directly measured plant height, with the most accurate estimation of millet plant height achieving an R2 of 0.873 and RMSE of 7.511 cm. Spectral parameters, canopy temperature, and plant height showed a high correlation with the AGB, and the correlation with the AGB was significantly improved after texture features were linearly transformed. Among single-factor features, the RF model based on textural indices showcased the highest accuracy in estimating the AGB (R2 = 0.698, RMSE = 0.323 kg m−2, and RPD = 1.821). When integrating two features, the RF model incorporating textural indices and canopy temperature data demonstrated optimal performance (R2 = 0.801, RMSE = 0.253 kg m−2, and RPD = 2.244). When the three features were fused, the RF model constructed by fusing spectral parameters, texture indices, and canopy temperature data was the best (R2 = 0.869, RMSE = 0.217 kg m−2, and RPD = 2.766). The RF model based on spectral parameters, texture indices, canopy temperature, and plant height had the highest accuracy (R2 = 0.877, RMSE = 0.207 kg m−2, and RPD = 2.847). In this study, the complementary and synergistic effects of multi-source remote sensing data were leveraged to enhance the accuracy and stability of the biomass estimation model.

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.

Abstract Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC.

Osthole is a natural coumarin compound which isolated from Cnidium monnieri (L.) Cusson, has extensive pharmacological activities and could be used as a leading compound for drug research and development. In a continuous effort to develop new acetylcholinesterase inhibitors from natural products, eighteen osthole esters were designed, synthesized, and confirmed by 1H NMR, 13C NMR and HRMS. The anti-AChE activity of These derivatives was measured at a concentration of 1.0 mol/mL in vitro by Ellman's method, and the result showed that 4m and 4o had moderate inhibitory activities with 68.8% and 62.6%, respectively. Molecular docking study results further revealed AChE interacted optimally with docking poses 4m and 4o. Network pharmacology also predicted that compound 4m could be involved in Ras signaling pathway, which made it a potential therapeutic target of AD.

Improper optimization of the rates and ratios of nitrogen application reduces grain yields and increases the nitrogen loss, thereby affecting environmental quality. In addition, scarcer evidence exists on the integrative approach of nitrogen, which could have effects on the biochemical and physiological characteristics of wheat. Treatments were arranged as nitrogen (N) rates of 00, 75, 150, 225, and 300 kg ha−1 in the main plots, and different nitrogen ratios were organized in subplots at 5:5:0:0 and 6:4:0:0, which were applied at the sowing, jointing, flowering, and grain filling stages. The results revealed that 225 kg N ha−1 significantly enhanced the stomatal conductance (Gs), photosynthetic rate (Pn), intercellular CO2 (Ci), transpiration rate (Tr), and total chlorophyll by 28.5%, 42.3%, 10.0%, 15.2%, and 50%, receptively, at the jointing stage in comparison to the control (0 kg N ha−1). Nitrogen application of 225 kg ha−1 increased the soil–plant analysis development (SPAD) value and the chlorophyll a, chlorophyll b, and carotenoid contents of winter wheat under the 6:4:0:0 ratio. The trend of the photosynthetic characteristics was observed to be greater at the 6:4:0:0 fertilization ratio compared to that at 5:5:0:0. The photosynthetic rate was significantly associated with the biochemical and physiological characteristics of winter wheat. In conclusion, the nitrogen dose of 225 kg ha−1 and the ratio of 6:4:0:0 (quantity applied at the sowing, jointing, flowering, and grain filling stages) effectively promoted the photosynthetic and other physiological characteristics of winter wheat.

Rapid and non-destructive estimation of leaf nitrogen accumulation (LNA) is essential to field nitrogen management. Currently, many vegetation indices have been used for indicating nitrogen status. Few studies systematically analyzed the performance of vegetation indices of winter wheat in estimating LNA under different irrigation regimes. This study aimed to develop a new spectral index for LNA estimation. In this study, 2 years of field experiments with different irrigation regimes were conducted from 2015 to 2017. The original reflectance (OR) and three transformed spectra [e.g., the first derivative reflectance (FDR), logarithm of the reciprocal of the spectra (Log(1/R)), and continuum removal (CR)] were used to calculate two- and three-band spectral indices. Correlation analyses and univariate linear and non-linear regression between transformed-based spectral indices and LNA were performed. The performance of the optimal spectral index was evaluated with classical vegetation index. The results showed that FDR was the most stable transformation method, which can effectively enhance the relationships to LNA and improve prediction performance. With a linear relationship with LNA, FDR-based three-band spectral index 1 (FDR-TBI1) (451, 706, 688) generated the best performance with coefficient of determination (R2) of 0.73 and 0.79, the root mean square error (RMSE) of 1.267 and 1.266 g/m2, and the ratio of performance to interquartile distance (RPIQ) of 2.84 and 2.71 in calibration and validation datasets, respectively. The optimized spectral index [FDR-TBI1 (451, 706, 688)] is more effective and might be recommended as an indicator for estimating winter wheat LNA under different irrigation regimes.

Barbatic acid, a compound isolated from lichen, has demonstrated a variety of biological activities. In this study, a series of esters based on barbatic acid (6a–q′) were designed, synthesized, and evaluated for their diuretic and litholytic activity at a concentration of 100 μmol/L in vitro. All target compounds were characterized using 1H NMR, 13C NMR, and HRMS, and the spatial structure of compound 6w was confirmed using X-ray crystallography. The biological results showed that some derivatives, including 6c, 6b′, and 6f′, exhibited potent diuretic activity, and 6j and 6m displayed promising litholytic activity. Molecular docking studies further suggested that 6b′ had an optimal binding affinity to WNK1 kinases related to diuresis, while 6j could bind to the bicarbonate transporter CaSR through a variety of forces. These findings indicate that some barbatic acid derivatives could be further developed into novel diuretic agents.

A simple and efficient hydroxide-mediated SNAr rearrangement was reported to synthesize new depside derivatives containing the diaryl ether skeleton from the natural product barbatic acid. The prepared compounds were determined using 1H NMR, 13C NMR, HRMS, and X-ray crystallographic analysis and were also screened in vitro for cytotoxicity against three cancer cell lines and one normal cell line. The evaluation results showed that compound 3b possessed the best antiproliferative activity against liver cancer HepG2 cell line and low toxicity, which made it worth further study.

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.

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.

Wheat is the third most producing crop in China after maize and rice. In order to enhance the nitrogen use efficiency (NUE) and grain yield of winter wheat, a two-year field experiment was conducted to investigate the effect of different nitrogen ratios and doses at various development stages of winter wheat (Triticum aestivum L.). A total of five N doses (0, N75, N150, N225, and N300 kg ha−1) as main plots and two N ratios were applied in split doses (50%:50% and 60%:40%, referring to 50% at sowing time and 50% at jointing stage, 50% at sowing time + 50% at flowering stage, 50% at sowing time + 50% at grain filling stage, and 60% + 40% N ratio applied as a 60% at sowing time and 40% at jointing stage, 60% at sowing time and 40% at flowering stage, and 60% at sowing time and 40% at grain filling stage in subplots). The results of this study revealed that a nitrogen dose of 225 kg ha−1 significantly augmented the plant height by 27% and above ground biomass (ABG) by 24% at the grain filling stage, and the leaf area was enhanced by 149% at the flowering stage under 60 + 40% ratios. Furthermore, the N225 kg ha−1 significantly prompted the photosynthetic rate by 47% at the jointing and flowering stages followed by grain filling stage compared to the control. The correlation analysis exhibited the positive relationship between nitrogen uptake and nitrogen content, chlorophyll, and dry biomass, revealing that NUE enhanced and ultimately increased the winter wheat yield. In conclusion, our results depicted that optimizing the nitrogen dose (N225 kg/ha−1) with a 60% + 40% ratio at jointing stage increased the grain yield and nitrogen utilization rate.

To overcome the limitations of traditional data collection methods in large-scale farmland wireless sensor network, in this study, we introduce a mobile sink and propose a virtual potential field-based strategy for mobile sink path planning. Virtual potential field-based strategy constructs a virtual field based on the residual energy, data generation rate, location information and cache urgency of nodes in the monitoring area. The stronger the virtual field, the more attractive it will be to mobile sink, which consequently affects the mobile path of sink node. Rendezvous points are selected in accordance with the maximum-farthest criterion, and the shortest path connecting all rendezvous points is taken as the mobile path of sink. Furthermore, the monitoring nodes employ the distance probability transmission strategy to have the transmission moment selected and the energy consumption optimized with reference to the path control information sent by the sink. The virtual potential fields and the rendezvous points are recalculated periodically according to the dynamic changes of both the node residual energy and the real-time cache. The simulation results showed that excellent transmission efficiency and network lifetime, and the combination of virtual potential field-based strategy and distance probability transmission strategy can have the fairness and real time of nodes guaranteed, thus it may meet the needs of large-scale farmland data collection.

(1) Background: With the increase in people’s consumption of processed oat products, the production of selenium (Se)-enriched oat has become a possibility to supplement the human body with Se. Therefore, the effects of various factors on the Se-enriched ability and yield of different oat varieties were comprehensively studied. (2) Methods: cv.“Pinyan 5” and cv.“Bayou 18” were applied at the stem-elongation stage and heading stage in the Jinzhong (JZ), and cv.“Bayou 1” and cv.“Jinyan 18” were applied at the heading stage and flowering stage in the northwestern Shanxi (JXB) with different doses of Na2SeO3 (0, 5.48, 10.96, 21.92, 43.84, 65.76, 98.64, 0, 5.48, 10.96, 21.92, 43.84, 65.76, 98.64, 147.96 g hm−2) by soil application and foliar spraying. (3) Results: The grain Se content and yield of oat were affected by the variety, Se application dose, stage and method of Se supplementation. Additionally, the Se content in oat grain was positively correlated with the Se application dose while the yield of oat first increased and then decreased with the Se application dose. (4) Conclusions: In the JZ and JXB, 21.92 g hm−2 and 43.84 g hm−2 Se was sprayed on the leaves of cv.“Bayou 18” and cv.“Bayou 1” at the heading stage, respectively, was the most effective Se biofortification program.

The application of organic manures was found to be beneficial, however, the integrated use of organic manures with chemical nitrogen fertilizers has proven more sustainable in increasing the photosynthetic attributes and grain yield of the winter-wheat crop. A multi-factor split-plot design was adopted, nitrogen and manure fertilizer treatments were set in the sub-plots, including nitrogen-gradient treatment of T1:0 kg N ha−1, T2:100 kg N ha−1, T3:200 kg N ha−1, and T4:300 kg N ha−1 (pure nitrogen -fertilizer application) The 25% reduction in nitrogen combined with the manure-fertilizer application includes T5:75 kg N ha−1 nitrogen and 25 kg N ha−1 manure, T6:150 kg N ha−1 nitrogen and 50 kg N ha−1 manure, and T7:225 kg N ha−1 nitrogen and 75 kg N ha−1 manure. The maximum results of the total chlorophyll content and photosynthetic rate were 5.73 mg/g FW and 68.13 m mol m−2 s−1, observed under T4 in Zhongmai 175, as compared to Jindong 22 at the heading stage. However, the maximum results of intercellular CO2 concentration were 1998.47 μmol mol−1, observed under T3 in Jindong 22, as compared to Zhongmai 175 at the tillering stage. The maximum results of LAI were 5.35 (cm2), observed under T7 in Jindong 22, as compared to Zhongmai 175 at the booting stage. However, the maximum results of Tr and Gs were 6.31 mmol H2O m−2 s−1 and 0.90 H2O mol m−2 s−1, respectively, observed under T7 in Zhongmai 175 as compared to Jindong 22 at the flowering stage. The results revealed that grain yield 8696.93 kg ha−1, grains spike−1 51.33 (g), and 1000-grain weight 39.27 (g) were significantly higher, under T3 in Zhongmai 175, as compared to Jindong 22. Moreover, the spike number plot−1 of 656.67 m2 was significantly higher in Jindong 22, as compared to Zhongmai 175. It was concluded from the study that the combined application of nitrogen and manure fertilizers in winter wheat is significant for enhancing seed at the jointing and flowering stages. For increased grain yield and higher economic return, Zhongmai 175 outperformed the other cultivars examined. This research brings awareness toward the nitrogen-fertilizer-management approach established for farmers’ practice, which might be observed as an instruction to increase agricultural management for the winter-wheat-growth season.

Real-time, nondestructive, and accurate estimation of plant water status is important to the precision irrigation of winter wheat. The objective of this study was to develop a method to estimate plant water content (PWC) by using canopy spectral proximal sensing data. Two experiments under different water stresses were conducted in 2014–2015 and 2015–2016. The PWC and canopy reflectance of winter wheat were collected at different growth stages (the jointing, booting, heading, flowering, and filling stages in 2015 and the jointing, booting, flowering, and filling stages in 2016). The performance of different spectral transformation approaches was further compared. Based on the optimal pretreatment, partial least squares regression (PLSR) and four combination methods [i.e., PLSR-stepwise regression (SR), PLSR-successive projections algorithm (SPA), PLSR-random frog (RF), and PLSR-uninformative variables elimination (UVE)] were used to extract the sensitive bands of PWC. The results showed that all transformed spectra were closely correlated to PWC. The PLSR models based on the first derivative transformation method exhibited the best performance (coefficient of determination in calibration, R2C = 0.96; root mean square error in calibration, RMSEC = 20.49%; ratio of performance to interquartile distance in calibration, RPIQC = 9.19; and coefficient of determination in validation, R2V = 0.86; root mean square error in validation, RMSEV = 46.27%; ratio of performance to interquartile distance in validation, RPIQV = 4.34). Among the combination models, the PLSR model established with the sensitive bands from PLSR-RF demonstrated a good performance for calibration and validation (R2C = 0.99, RMSEC = 11.53%, and RPIQC = 16.34; and R2V = 0.84, RMSEV = 44.40%, and RPIQV = 4.52, respectively). This study provides a theoretical basis and a reference for estimating PWC of winter wheat by using canopy spectral proximal sensing data.

The accuracy of nitrogen (N) diagnosis is essential to improve N use efficiency. The standard critical N concentration (standard Nc) dilution curves, an expression of the dynamics of N uptake and dry matter accumulation in plants, are widely used to diagnose the N status of crops. Several standard Nc dilution curves were proposed and validated for several crops, based on experiments involving different N fertilizer treatments. However, standard Nc dilution curves are affected by crop water status, e.g., resulting from differences in irrigation management. This paper aimed at developing a N diagnostic model under the coupling effect of irrigation and fertilizer managements. For this purpose, Nc dilution curves were developed under different irrigation rates. Additionally, plant water content (PWC), leaf water content (LWC), leaf area index (LAI), equivalent water thickness (EWT), and leaf area duration (LAD) were introduced into the model, to construct a modified Nc (mNc) dilution curve. The mNc dilution curves were designed using the principle of hierarchical linear model (HLM), introducing aboveground dry biomass (AGB) as the first layer of information, whereas the second layer of information included the different agronomic variables (PWC, LWC, LAI, EWT, and LAD). The results showed that parameters “a” and “b” of the standard Nc dilution curves ranged from 5.17 to 6.52 and −0.69 to −0.38 respectively. Parameter “a” was easily affected by different management conditions. The performance of standard Nc dilution models obtained by the cross-validation method was worse than that of mNc dilution models. The Nc dilution curve based on 4 years of data was described by the negative power equation Nc = 5.05 × AGB–0.47, with R2 and nRMSE of 0.63 and 0.21, respectively. The mNc dilution curve considers different treatments and was represented by the equation mNc = a×AGB−b, where a = 2.09 × PWC + 3.24, b = −0.02 × LAI + 0.51, with R2 and nRMSE of 0.79 and 0.13, respectively. For winter wheat, C3 crop, there would be a few problems in using standard Nc dilution methods to guide field management, however, this study provides a reliable method for constructing mNc dilution curves under different water and N fertilizer management. Due to the significant differences in hereditary, CO2 fixation efficiency and N metabolism pathways for C3 and C4 crops, the construction of mNc dilution curve suitable for different N response mechanisms will be conducive to the sustainable N management in crop plants.

The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (2 m−2 and 0.0305 cm3 cm−3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m−2 and 0.0244 cm3 cm−3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha−1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha−1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

Rapid and non-destructive estimation of plant water status is essential for adjusting field practices and irrigation schemes of winter wheat. The objective of this study was to find new combination spectral indices based on canopy reflectance for the estimation of plant water status. Two experiments with different irrigation regimes were conducted in 2015-2016 and 2016-2017. The canopy spectra were collected at different growth stages of winter wheat. The raw and derivative reflectance of canopy spectra showed obvious responses to the change of plant water status. Except for equivalent water thickness (EWT), other water metrics had good relationships with new combination spectral indices (R2>0.7). An acceptable model of canopy water content (CWC) was established with the best spectral index (RVI (1605, 1712)). Models of leaf water content (LWC) and plant water content (PWC) had better performances. Optimal spectral index of LWC was FDRVI (687, 531), having R2, RMSE and RPD of 0.77, 2.181 and 2.09; R2, RMSE and RPD of 0.87, 2.652 and 2.34 for calibration and validation, respectively. And PWC could be well estimated with FDDVI (688, 532) (R2, RMSE and RPD of 0.79, 3.136 and 2.21; R2, RMSE and RPD of 0.83, 3.702 and 2.18 for calibration and validation, respectively). Comparing the performances of estimation models, the new combination spectral indices FDRVI (687, 531) based on canopy reflectance improved the accuracy of estimation of plant water status. Besides, based on FDRVI (687, 531), LWC was the optimal water metrics for plant water status estimation.

The growth status of winter wheat in irrigated field and rainfed field are obviously different and the field types may have an effect on the predictive accuracy of hyperspectral model. The objectives of the present study were to understand the difference of spectral sensitive wavelengths for leaf area index (LAI) in two field types and realize its hyperspectral prediction. In study, a total of 31 and 28 sample sites in irrigated fields and rainfed fields respectively were selected from Wenxi County, and the LAI and canopy spectra were also collected at the main grow stage of winter wheat. The method of successive projections algorithm (SPA) was applied by selecting the important wavelengths, and the multiple linear regression (MLR) and partial least squares regression (PLSR) were used to construct the predictive model based on the important wavelengths and full wavelengths, respectively. Moreover, the parameters of variable importance project (VIP) and B-coefficient derived from PLSR analysis were implemented to validate the evaluated wavelengths using the SPA method. The sensitive wavelengths of LAI for irrigated field and rainfed field were 404, 407, 413, 417, 450, 677, 715, 735, 816, 1127 and 404, 406, 432, 501, 540, 679, 727, 779, 1120, 1290 nm, respectively, and these wavelengths proved to be highly correlated with LAI. Compared with the model performance based on the SPA-MLR and PLSR methods, the method of SPA-MLR was proved to be better (rainfed field: R2 = 0.736, RMSE = 1.169, RPD = 1.6245; irrigated field: R2 = 0.716, RMSE = 1.059, RPD = 1.538). Moreover, the predictive model of LAI in rainfed fields had a better accuracy than the model in irrigated fields. The results from this study indicated that it was necessary to classify the field type while monitoring the winter wheat using the remote sensing technology. This study also demonstrated that the multivariate method of SPA-MLR could accurately evaluate the sensitive wavelengths and construct the predictive model of LAI.

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.

[ Objectives] The therapeutic potential and action mechanism of Folium Pyrrosiae were analyzed based on the biolabel pattern. | Methods] The chemical components of Folium Pyrrosiae were analyzed by liquid chromätograpliy-mass spectrometry (LC-MS). Ten databases. including Pubchem. CTD. BindingDB. HERB. TCMIP. ETCM. SwissTargetPrediction. SuperPred webserver. TargetNet and SEA. were used in turn to retrieve the targets of related components. and key components were obtained according to the enrichment degree of targets. The obtained targets were imported into the STRING database to obtain PPI information and screen out core targets. The DAVID database was employed to analyze KEGG pathways of core targets and obtain key pathways. A key component-core target-key pathway network of Folium Pyrrosiae was constructed by Cytoscape3.10. 1 software. The obtained KEGG pathways were input into the CTD database to predict corresponding diseases, and discussion and analysis were carried out. [Results] Ten key components. 30 potential targets and 10 key pathways were screened out. and they participated in many diseases. of which five diseases were mainly analyzed. [Conclusions] Folium Pyrrosiae had the characteristic of multi-component, multi-target and multi-pathway synergistic effect in the treatment of lung cancer. type 2 diabetes. atherosclerosis. liver cancer. prostate cancer and other diseases. and the therapeutic potential and action mechanism of Folium Pyrrosiae were analyzed through the biolabel pattern. This study provides a research basis for further developing new functions of Folium Pyrrosiae. [ABSTRACT FROM AUTHOR]