Background: To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods: The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results: The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions: The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

Glutamine synthetase (GS) isozymes play critical roles in nitrogen (N) metabolism. However, the exact relationship between GS and nitrogen use efficiency (NUE) remain unclear. We have selected and compared two wheat cultivars, YM49 and XN509, which were identified as the N-efficient and N-inefficient genotypes, respectively. In this study, agronomical, morphological, physiological and biochemical approaches were performed. The results showed that TaGS1 was high expressed post-anthesis, and TaGS2 was highly expressed pre-anthesis in N-efficient genotype compared to N-inefficient genotype. GS1 and GS2 isozymes were also separated by native-PAGE and found that the spatial and temporal distribution of GS isozymes, their expression of gene and protein subunits in source-sink-flow organs during development periods triggered the pool strength and influenced the N flow. According to the physiological role of GS isozymes, we illustrated four metabolic regulation points, by which acting collaboratively in different organs, accelerating the transport of nutrients to the grain. It suggested that the regulation of GS isozymes may promote flow strength and enhance NUE by a complex C-N metabolic mechanism. The relative activity or amount of GS1 and GS2 isozymes could be a potential marker to predict and select wheat genotypes with enhanced NUE.

Background: Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by creating four categories of image labels: non-wheat ear, one wheat ear, two wheat ears, and three wheat ears, which was then was sent into the convolution neural network (CNN) model for training and testing to reduce the complexity of the model. Results: The recognition accuracy of non-wheat, one wheat, two wheat ears, and three wheat ears were 99.8, 97.5, 98.07, and 98.5%, respectively. The model R² reached 0.96, the root mean square error (RMSE) was 10.84 ears, the macro F1-score and micro F1-score both achieved 98.47%, and the best performance was observed during late grain-filling stage (R² = 0.99, RMSE = 3.24 ears). The model could also be applied to the UAV platform (R² = 0.97, RMSE = 9.47 ears). Conclusions: The classification of segmented images as opposed to target recognition not only reduces the workload of manual annotation but also improves significantly the efficiency and accuracy of wheat ear counting, thus meeting the requirements of wheat yield estimation in the field environment.

Background: The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Methods: In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. Results: The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R² of 0.85 and R² of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R² and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). Conclusion: The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.

The unmanned aerial vehicle (UAV) image spectral information and texture feature (TF) information were fused to develop an improved model for winter wheat leaf nitrogen content (LNC) monitoring model to provide a reference for wheat nitrogen status monitoring and accurate management. The data of wheat LNC and UAV-hyperspectral imaging were simultaneously obtained at the main growth stages (jointing, booting, and filling stages) of various winter wheat varieties under various nitrogen fertilizer treatments. The correlation between the vegetation indexes (VIs) in combination of any two bands, the TFs, and LNC were systematically analyzed. Then, the optimal VIs and TFs without multicollinearity problems were screened using a variance inflation factor (VIF) to form a ‘graph–spectrum’ fusion index. Four machine learning methods, namely ridge regression (RR), partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), were used to construct respective quantitative winter wheat LNC estimation models. The results revealed that the model for estimating LNC constructed using the ‘graph–spectrum’ information formed by eight parameters, including the normalized vegetation index NDVI (R₅₇₈, R₄₉₀), the difference vegetation index DVI (R₈₃₀, R₇₇₈), MEA₄₉₀, MEA₇₇₈, VAR₄₉₀, VAR₅₇₈, VAR₇₇₈, and HOM₅₇₈ as input and the RR algorithm, performed the best. It outperformed the models developed by the implementation of VIs and TFs as input. The coefficient of determination (R²), root mean square error (RMSE), and relative percent deviation (RPD) of the calibration set were 0.84, 0.25, and 2.50, correspondingly, and those of the validation set were 0.87, 0.27, and 2.33, respectively. The model of winter wheat LNC constructed by fusing spectral and TF information considerably improved the prediction accuracy. The present research results provide a basis and reference for the application of UAV hyperspectral technology in wheat nitrogen nutrient monitoring.