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Crop aboveground biomass (AGB) is one of the most important indicators in crop breeding and crop management, and can be used for crop yield prediction. A number of vegetation indices (VIs) have been proposed to estimate crop biomass, but they perform poorly at high biomass levels and are easily affected by background materials. Texture analysis has been proved to be an efficient approach in forest biomass estimation, but has never been applied to crops with low-altitude unmanned aerial vehicle (UAV) images. The objective of this study was to improve rice AGB estimation by combining textural and spectral analysis of UAV imagery. A two-year rice experiment was conducted in 2015 and 2016, involving different nitrogen (N) rates, planting densities and rice cultivars with three replicates. A six-band multispectral (MS) camera was mounted on a UAV to acquire rice canopy images at critical stages during the rice growing seasons and concurrent field samplings were taken. Simple regression and stepwise multiple linear regression models were developed between biomass data from the two-year experiment and image parameters derived from four different types of feature sets. These features represented commonly used VIs, texture parameters, normalization of texture measurements (normalized difference texture index, NDTI) and combinations of VIs and NDTIs. Finally, all the regression models were evaluated by cross-validation over pooled data with the coefficient of determination (R2) and the root mean square error (RMSE). Results demonstrated that the optimized soil adjusted vegetation index (OSAVI) exhibited the best relationship with AGB for the whole season (R2 = 0.63) and post-heading stages (R2 = 0.65). Red-edge-based indices yielded best performance (R2 > 0.70) only for the growth stages before heading. The texture measurement mean (MEA) from the NIR band was the best among the eight candidates in AGB estimation. Texture index (NDTI (MEA800, MEA550)) was superior to all the evaluated VIs in estimating AGB for the whole season (R2 = 0.75) and pre-heading stages (R2 = 0.84). Further improvement was obtained across the whole season by combining NDTIs and VIs through a multiple linear regression. This multivariate model produced the highest estimation accuracy for all stages (R2 = 0.78 and RMSE = 1.84 t ha−1) and different stage groups (R2 = 0.84 and RMSE = 1.06 t ha−1 for pre-heading stages and R2 = 0.65 and RMSE = 1.94 t ha−1 for post-heading stages). The findings imply that the integration of textural information with spectral information significantly improves the accuracy for rice biomass estimation compared to the use of spectral information alone.

In China’s second-largest wheat-producing region, the mid-lower Yangtze River area, cold stress impacts winter wheat production during the pre-heading growth stage. Previous research focused on specific growth stages, lacking a comprehensive approach. This study utilizes Unmanned Aerial Vehicle (UAV) multispectral imagery to monitor Soil-Plant Analysis Development (SPAD) values throughout the pre-heading stage, assessing crop stress resilience. Vegetation Indices (VIs) and Texture Indices (TIs) are extracted from UAV imagery. Recursive Feature Elimination (RFE) is applied to VIs, TIs, and fused variables (VIs + TIs), and six machine learning algorithms are employed for SPAD value estimation. The fused VIs and TIs model, based on Long Short-Term Memory (LSTM), achieves the highest accuracy (R2 = 0.8576, RMSE = 2.9352, RRMSE = 0.0644, RPD = 2.6677), demonstrating robust generalization across wheat varieties and nitrogen management practices. This research aids in mitigating winter wheat frost risks and increasing yields.

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice ( L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages ( = 0.52-0.70), and photochemical reflectance index and blue N index from ground (PRI and BNI ) performed consistently well across all growth stages ( = 0.48-0.65 and 0.39-0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNI and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring.

Nitrogen (N) is critical for maize (Zea mays L.) growth and yield, necessitating precise estimation of canopy nitrogen concentration (CNC) to optimize fertilization strategies. Remote sensing technologies, such as proximal hyperspectral sensors and unmanned aerial vehicle (UAV)-based multispectral imaging, offer promising solutions for non-destructive CNC monitoring. This study evaluates the effectiveness of proximal hyperspectral sensor and UAV-based multispectral data integration in estimating CNC for spring maize during key growth stages (from the 11th leaf stage, V11, to the Silking stage, R1). Field experiments were conducted to collect multispectral data (20 vegetation indices [MVI] and 24 texture indices [MTI]), hyperspectral data (24 vegetation indices [HVI] and 20 characteristic indices [HCI]), alongside laboratory analysis of 120 CNC samples. The Boruta algorithm identified important features from integrated datasets, followed by correlation analysis between these features and CNC and Random Forest (RF)-based modeling, with SHAP (SHapley Additive exPlanations) values interpreting feature contributions. Results demonstrated the UAV-based multispectral model achieved high accuracy and Computational Efficiency (CE) (R2 = 0.879, RMSE = 0.212, CE = 2.075), outperforming the hyperspectral HVI-HCI model (R2 = 0.832, RMSE = 0.250, CE =2.080). Integrating multispectral and hyperspectral features yields a high-precision model for CNC model estimation (R2 = 0.903, RMSE = 0.190), outperforming standalone multispectral and hyperspectral models by 2.73% and 8.53%, respectively. However, the CE of the integrated model decreased by 1.93% and 1.68%, respectively. Key features included multispectral red-edge indices (NREI, NDRE, CI) and texture parameters (R1m), alongside hyperspectral indices (SR, PRI) and spectral parameters (SDy, Rg) exhibited varying directional impacts on CNC estimation using RF. Together, these findings highlight that the Boruta–RF–SHAP strategy demonstrates the synergistic value of integrating multi-source data from UAV-based multispectral and proximal hyperspectral sensing data for enhancing precise nitrogen management in maize cultivation.

Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB.

Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.

Monitoring winter wheat Soil–Plant Analysis Development (SPAD) values using Unmanned Aerial Vehicles (UAVs) is an effective and non-destructive method. However, predicting SPAD values during the booting stage is less accurate than other growth stages. Existing research on UAV-based SPAD value prediction has mainly focused on low-altitude flights of 10–30 m, neglecting the potential benefits of higher-altitude flights. The study evaluates predictions of winter wheat SPAD values during the booting stage using Vegetation Indices (VIs) from UAV images at five different altitudes (i.e., 20, 40, 60, 80, 100, and 120 m, respectively, using a DJI P4-Multispectral UAV as an example, with a resolution from 1.06 to 6.35 cm/pixel). Additionally, we compare the predictive performance using various predictor variables (VIs, Texture Indices (TIs), Discrete Wavelet Transform (DWT)) individually and in combination. Four machine learning algorithms (Ridge, Random Forest, Support Vector Regression, and Back Propagation Neural Network) are employed. The results demonstrate a comparable prediction performance between using UAV images at 120 m (with a resolution of 6.35 cm/pixel) and using the images at 20 m (with a resolution of 1.06 cm/pixel). This finding significantly improves the efficiency of UAV monitoring since flying UAVs at higher altitudes results in greater coverage, thus reducing the time needed for scouting when using the same heading overlap and side overlap rates. The overall trend in prediction accuracy is as follows: VIs + TIs + DWT > VIs + TIs > VIs + DWT > TIs + DWT > TIs > VIs > DWT. The VIs + TIs + DWT set obtains frequency information (DWT), compensating for the limitations of the VIs + TIs set. This study enhances the effectiveness of using UAVs in agricultural research and practices.

In order to evaluate forest quality and carbon stocks and improve our understanding of ecosystems and carbon cycling processes, the accurate measurement of aboveground biomass (AGB) and other forest characteristics is crucial. This paper considers the response differences between the bands obtained from Landsat 8 and Sentinel-2A sensors, respectively, and combines the exhaustive combination of spectral indices with normalization and ratio techniques to establish suitable weights for the bands in the vegetation index using relative sensitivity and noise equivalent (NE) to improve the saturation effect between the vegetation index and forest parameters (canopy closure (CC), forest stand density (S), basal area (BA), and AGB) and extend the linear relationship between them. This paper also considers the effects of window size, direction, and principal component analysis on texture features, adds weight to textures and combines textures using linear correlation and NE, establishes texture indices to improve the limitations of information contained in individual texture features, analyzes the potential of texture features to evaluate each forest parameter under different conditions, and better captures the variation of forest parameters. In this paper, we only analyze the planted coniferous forest in Saihanba to avoid the differences in electromagnetic wave effects that are difficult to judge and analyze because of the differences in leaf size and leaf orientation between coniferous and broad-leaf forests. In contrast, the vegetation indices and texture indices obtained from Sentinel-2A could better estimate each vegetation parameter, and the linear estimation of each vegetation parameter using the new texture index reached an R2 above 0.65. The results of this study indicate that Sentinel-2A and Landsat 8 are promising remote sensing datasets for estimating vegetation parameters at the regional scale, and Sentinel-2A data can be employed as the primary source of earth observation data for assessing forest resources in the Saihanba area.

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models.