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Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model's prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion. [ABSTRACT FROM AUTHOR]

Nitrogen has significant effect on the growth and development in crop, the formation of yield and quality. Precision diagnosis and dynamic regulation of crop is the important content and scientific basis of precision agriculture. Thus, predicting crop N status accurately and applying appropriate rate N to crop are the focus for many studies in agricultural sciences. The crop canopy nitrogen status estimation based on spectroscopy is important tool for crop nitrogen management, but its accuracy of estimation is often affected by other factors such as canopy structures. The nitrogen nutrition index (NNI) was sensitive to nitrogen status because it combined the information of plant nitrogen content, the individual character of crop and biomass, and the group character of crop. The traditional methods for calculation of plant N concentration and aboveground biomass are done manually and they are time consuming. Thus, it is hard to apply NNI in precise farming. Recently, it has been documented remote sensing technology can be used to assess many biophysical and biochemical variable of crops, especially through spectral indices. NNI was considered a good indicator of crop nitrogen status and provided new opportunities for hyperspectral applications. The objective of this study was on the NNI estimation through remote sensing spectral parameters sensitive to leaf N content and canopy nitrogen density (CND). Based on the field experiments of different N rates and varieties of winter wheat from booting to filling stages, the relationships between spectral indices and leaf N and CND status in wheat were analyzed to determine the key spectral indices for assessment of leaf N content and CND. These relationships can help accurately quantitative diagnosis of nitrogen status, and provide the reference for the estimation of fertilizer rate and crop yield and quality. Upon the analysis the empirical model for NNI estimation based on the optimal parameters of leaf N and CND was established and evaluated. The results showed that, Red edge position based on linear interpolation method (REPLI), modified red edge simple ratio index (mSR705), ratio index-1dB (RI-1dB), simple ratio pigment index (SRPI), Vogelman red edge index (VOG) and other indicators had a good correlation with winter wheat nitrogen nutrition (r⩾0.85), and this correlation was not affected by growing period. Therefore, they can be used to evaluate the nutritional status of canopy nitrogen inversion. Then the optimal spectral parameters were selected and the nitrogen index regression models were established. Independent experimental data was used for model validation. The results showed that REPLI in the nitrogen nutrition index estimation performed better (r=0.927, p<0.01), and the model estimation accuracy was high (R2=0.859, RMSE=0.078). Our research indicated that NNI had advantages in the field crop nitrogen nutrition diagnosis, and it had potential in qualitative and quantitative diagnosis of nitrogen nutrition status by hyperspectral inversion. [ABSTRACT FROM AUTHOR]

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening. [ABSTRACT FROM AUTHOR]