Unmanned aerial vehicles (UAV) has been increasingly applied to crop growth monitoring due to their advantages, such as their rapid and repetitive capture ability, high resolution, and low cost. LAI is an important parameter for evaluating crop canopy structure and growth without damage. Accurate monitoring of cotton LAI has guiding significance for nutritional diagnosis and the accurate fertilization of cotton. This study aimed to obtain hyperspectral images of the cotton canopy using a UAV carrying a hyperspectral sensor and to extract effective information to achieve cotton LAI monitoring. In this study, cotton field experiments with different nitrogen application levels and canopy spectral images of cotton at different growth stages were obtained using a UAV carrying hyperspectral sensors. Hyperspectral reflectance can directly reflect the characteristics of vegetation, and vegetation indices (VIs) can quantitatively describe the growth status of plants through the difference between vegetation in different band ranges and soil backgrounds. In this study, canopy spectral reflectance was extracted in order to reduce noise interference, separate overlapping samples, and highlight spectral features to perform spectral transformation; characteristic band screening was carried out; and VIs were constructed using a correlation coefficient matrix. Combined with canopy spectral reflectance and VIs, multiple stepwise regression (MSR) and extreme learning machine (ELM) were used to construct an LAI monitoring model of cotton during the whole growth period. The results show that, after spectral noise reduction, the bands screened by the successive projections algorithm (SPA) are too concentrated, while the sensitive bands screened by the shuffled frog leaping algorithm (SFLA) are evenly distributed. Secondly, the calculation of VIs after spectral noise reduction can improve the correlation between vegetation indices and LAI. The DVI (540,525) correlation was the largest after standard normal variable transformation (SNV) pretreatment, with a correlation coefficient of −0.7591. Thirdly, cotton LAI monitoring can be realized only based on spectral reflectance or VIs, and the ELM model constructed by calculating vegetation indices after SNV transformation had the best effect, with verification set R² = 0.7408, RMSE = 1.5231, and rRMSE = 24.33%, Lastly, the ELM model based on SNV-SFLA-SNV-VIs had the best performance, with validation set R² = 0.9066, RMSE = 0.9590, and rRMSE = 15.72%. The study results show that the UAV equipped with a hyperspectral sensor has broad prospects in the detection of crop growth index, and it can provide a theoretical basis for precise cotton field management and variable fertilization.

Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R² was 0.9109, and RMSE was 0.91277 t.ha⁻¹. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha⁻¹. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R², RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R², RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R² = 0.80 and RMSE = 1.67 g kg⁻¹ of the Xinluzao 45 optimal model, and R² = 0.42 and RMSE = 3.13 g kg⁻¹ of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved.

Early diagnosis of cotton verticillium wilt (VW) and accurate assessment of the disease degree are important prerequisites for preventing the large-scale development of cotton VW. Hyperspectral techniques have been widely used for monitoring the extent of plant diseases, but early detection of VW disease in cotton remains a challenge. In this study, the Boruta algorithm was used to select the key physiological characteristics (leaf temperature, chlorophyll a content, and equivalent water thickness) of cotton leaves at the early stage of VW disease, and then the Relief-F algorithm was used to select the spectral features indicating multiple “symptoms” of cotton VW disease at the early stage. In addition, a new cotton VW early monitoring indicator (CVWEI) was constructed by combining the weights of the new index and related bands using a hierarchical analysis (AHP) and entropy weighting method (EWM). The study showed that the physiological indices constructed under VW stress were better indicators of VW disease than traditional vegetation indices; CVEWI achieved a high accuracy of 95% in the test set, with a Kappa coefficient of 0.89; and the test set R² was 0.73 and RMSE was 3.15% for monitoring disease severity, compared to the optimal classification constructed using a single spectral index. The results may provide new ideas and methods for early and accurate monitoring of VW and other fungal diseases.

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.