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 (R 2 >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 R 2 , RMSE and RPD of 0.77, 2.181 and 2.09; R 2 , 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) (R 2 , RMSE and RPD of 0.79, 3.136 and 2.21; R 2 , 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.

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

The deformation characteristics and the roll force are of significant importance in the rolling of seamless steel tubes with a tandem skew rolling (TSR) process. In this paper, finite element methods and dual stream functions are utilized to analyze tube rolling with the TSR process. Finite element simulations reveal the deformation characteristics of the seamless steel tube during the rolling process. In addition, the kinematically admissible velocity field and the strain rate field of the deforming tube are described and derived in terms of the dual stream functions. The roll force in theoretical analysis is obtained through upper bound analysis. Experiments are carried out in a TSR testing mill on a carbon steel, to show the validity of the analysis. The curve of the roll force in the process is obtained and the variation of the roll forces is analyzed in detail. Through the comparison between the theoretical, numerical, and experimental investigations, the constructed analysis model is used to study the TSR process and establish a theoretical basis for further development of the process.

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.

Along with a rapid growth of cloud computing technology and its deep application in Agriculture Intelligent Information System, Agriculture Industry information security and privacy has become a highlight of the issue about Agriculture Cloud Information System. Encrypting is a conventional information security means, however, hitherto almost all encryption scheme cannot support the operation based on cipher-text. As a result, it is a difficult to build up the corporate and individual information security and privacy-securing in the information system based on cloud computing platform. In order to construct the information security and privacy of cloud computing infrastructure, down to the practicality of Agriculture Information System the project crew brings forward An Innovative Encryption Method for Agriculture Intelligent Information System based on Cloud Computing Platform, OCEVMO for short, which takes root in the theory of matrix, and supports a series of cipher-text-operation essential to build a secure communication protocol between user, owner and cloud server. Beside the conventional encryption-decryption operation, OCEVMO implements 4 operations of cipher-textnumerical-value data such as adding, subtracting, multiplying and dividing. Theoretical analysis and experimental performance estimation demonstrates that OCEVMO is of IND-CCA security, capable of performing crypo-function with a moderate speed. Its favorable versatile performance gives promise of the interactive operation Securing corporateindividual privacy in the area of Agriculture Intelligent Information System.