Search

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas. [ABSTRACT FROM AUTHOR]

Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton's late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, and deep learning methods are widely used to extract target objects from the complex background. Therefore, this study takes seedling cotton as the research object and uses three deep learning algorithms, YOLOv5, YOLOv7, and CenterNet, for cotton seedling detection and counting using images at six different times of the cotton seedling period based on multispectral images collected by UAVs to develop a model applicable to the whole cotton seedling period. The results showed that when tested with data collected at different times, YOLOv7 performed better overall in detection and counting, and the T4 dataset performed better in each test set. Precision, Recall, and F1-Score values with the best test results were 96.9%, 96.6%, and 96.7%, respectively, and the R2, RMSE, and RRMSE indexes were 0.94, 3.83, and 2.72%, respectively. In conclusion, the UAV multispectral images acquired about 23 days after cotton sowing (T4) with the YOLOv7 algorithm achieved rapid and accurate seedling detection and counting throughout the cotton seedling stage. [ABSTRACT FROM AUTHOR]

By studying the spectral information of cotton leaf nitrogen content, sensitive feature bands and spectral indices for leaf nitrogen content were screened, and different methods were used to model the screened feature bands and indices to find a method with higher accuracy and stability of the inversion model, which provides a theoretical basis and technical support for remote sensing estimation of cotton nitrogen content in Xinjiang. The experiment was conducted in 2019–2020 at the Second Company of Shihezi University Teaching Experimental Farm in Xinjiang, China, with six fertilization treatments (0, 120, 240, 360, 480 kg/hm pure N), sampled at five key fertility stages of cotton (squaring stage, full budding stage, flowering, boll stage, and boll opening stage), and the obtained data were used in two modeling approaches (eigenbands and spectral indices) to establish a cotton nitrogen estimation model and estimate the cotton leaf N content. The results showed that the nonlinear model using SVR was validated with an R2 of 0.71 and an RMSE of 3.91. The linear models of MLR and PLS were developed for the feature bands screened by SPA and RF, respectively, and the best modeling result was achieved by SPA-PLS with a validated R2 of 0.722 and an RMSE of 3.83. The existing spectral indices were optimized by screening the central wavelength and the simple linear regression model was constructed. The inversion effect of the SVR model with the characteristic spectral modeling was better than the index results. However, compared with the direct use of the characteristic wavelengths and the SVR way of modeling, the accuracy of leaf N content estimation by the model built by optimizing the spectral indices was reduced but the stability was greatly improved, and it can be used as a hyperspectral model for leaf N content at full fertility. The hyperspectral estimation of leaf N content in cotton can be used as a hyperspectral estimation method for the whole fertility period. [ABSTRACT FROM AUTHOR]