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.

Background: Fractional vegetation cover (FVC) is an important parameter for evaluating crop-growth status. Optical remote-sensing techniques combined with the pixel dichotomy model (PDM) are widely used to estimate cropland FVC with medium to high spatial resolution on the ground. However, PDM-based FVC estimation is limited by effects stemming from the variation of crop canopy chlorophyll content (CCC). To overcome this difficulty, we propose herein a “fan-shaped method” (FSM) that uses a CCC spectral index (SI) and a vegetation SI to create a two-dimensional scatter map in which the three vertices represent high-CCC vegetation, low-CCC vegetation, and bare soil. The FVC at each pixel is determined based on the spatial location of the pixel in the two-dimensional scatter map, which mitigates the effects of CCC on the PDM. To evaluate the accuracy of FSM estimates of the FVC, we analyze the spectra obtained from (a) the PROSAIL model and (b) a spectrometer mounted on an unmanned aerial vehicle platform. Specifically, we use both the proposed FSM and traditional remote-sensing FVC-estimation methods (both linear and nonlinear regression and PDM) to estimate soybean FVC. Results: Field soybean CCC measurements indicate that (a) the soybean CCC increases continuously from the flowering growth stage to the later-podding growth stage, and then decreases with increasing crop growth stages, (b) the coefficient of variation of soybean CCC is very large in later growth stages (31.58–35.77%) and over all growth stages (26.14%). FVC samples with low CCC are underestimated by the PDM. Linear and nonlinear regression underestimates (overestimates) FVC samples with low (high) CCC. The proposed FSM depends less on CCC and is thus a robust method that can be used for multi-stage FVC estimation of crops with strongly varying CCC. Conclusions: Estimates and maps of FVC based on the later growth stages and on multiple growth stages should consider the variation of crop CCC. FSM can mitigates the effect of CCC by conducting a PDM at each CCC level. The FSM is a robust method that can be used to estimate FVC based on multiple growth stages where crop CCC varies greatly.

Accurate estimations of soil cover parameters (fractional coverage of crop residue [f_cr], crop [f_c], and bare soil [f_bs]) in croplands can assist in decision-making for agricultural management organisations and are important for understanding the interrelations between global changes and terrestrial ecosystems. In recent decades, optical remote sensing has been widely used for mapping f_c, f_cr, and f_bs. The crop residue and soil spectra decrease rapidly with increasing cropland moisture primarily because of water absorption in the near-infrared and shortwave infrared bands. This leads to a decrease in the denominator of current normalized crop residue spectral indices (shortwave infrared [SWIR] band 1 + band 2), thereby causing them to increase with increasing cropland moisture. This study revealed that the slopes of the soil and crop residue spectra between SWIR1 and SWIR2 were less affected by cropland moisture changes. Based on this feature, we propose a shortwave infrared angle (SWIRA) index and jointly use it with normalized difference vegetation index (NDVI) to estimate and produce multi-temporal maps of f_c, f_cr, and f_bs using broadband Sentinel-2 MSI images. The proposed SWIRA is the slope of the spectrum between SWIR1 and SWIR2. This study (i) uses laboratory-based spectral reflectance to evaluate the performance of the SWIRA and (ii) further evaluates the performance of the SWIRA–NDVI method using multi-temporal Sentinel-2 MSI images. Our results indicate that (i) cropland moisture has a considerably smaller effect on SWIRA than it does on current broadband normalized crop residue spectral indices, and (ii) maps of the estimated f_c, f_cr, and f_bs values can be used to improve crop growth and decision-making, for example, by providing harvest maps and soil tillage intensity maps.

Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture.

Take-all is a root disease that can severely reduce wheat yield, and wheat leaves with take-all disease show a large amount of chlorophyll loss. The PROSAIL model has been widely used for the inversion of vegetation physiological parameters with a clear physical meaning of the model and high simulation accuracy. Based on the chlorophyll deficiency characteristics, the reflectance data under different canopy chlorophyll contents were simulated using the PROSAIL model. In addition, inverse models of spectral reflectance profiles and canopy chlorophyll contents were constructed using a one-dimensional convolutional neural network (1D-CNN), and a transfer learning approach was used to detect the take-all disease levels. The spectral reflectance data of winter wheat acquired by an airborne imaging spectrometer during the filling period were used as input parameters of the model to obtain the chlorophyll content of the canopy. Finally, the results of the distribution of winter wheat take-all disease were mapped based on the relationship between take-all disease and the chlorophyll content of the canopy. The results showed that classification based on the deep learning model performed well for winter wheat take-all monitoring. This study can provide some reference basis for high-precision winter wheat take-all disease monitoring and can also provide some technical method references and ideas for remote sensing crop pest and disease remote sensing mapping.

Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.