As a core carrier of the low-altitude economy in the agricultural and rural sectors, agricultural unmanned aircraft have experienced explosive growth in recent years and have become a vital technological force driving agricultural modernization and rural revitalization. While the industrial scale has expanded dramatically and technological applications have undergone rapid iteration, the development of supporting regulations and standards has lagged behind. Based on a review of the development of domestic and international regulations and standards for agricultural unmanned aerial vehicles, this paper systematically analyzes the legal framework for airspace management, airworthiness certification, and personnel qualifications-as well as the current status of the four-tier standards framework including national standards, industry standards, local standards, and group standards. The findings indicate that China has initially established a regulatory framework with the Interim Regulation on the Flight Management of Unmanned Aircraft as the overarching legislation, and a standards architecture with the Guidelines for the Construction of the Low-Altitude Economy Standards System as the top-level design. However, shortcomings remain in areas such as application scenario coverage, component and subsystem requirements, and supporting methodological standards. Looking into the future, efforts should be directed toward advancing risk-based classification and hierarchical management, accelerating the expansion of application scenarios and the refinement of methods within the standards system, and building a digital intelligent supervision system, thereby providing institutional safeguards for the high-quality development of the agricultural low-altitude economy.

Accurate quantitative prediction of crop yield is a crucial foundation for ensuring national food security and formulating smart agricultural decisions. During the rice growth cycle, the full heading stage is the most critical period for biomass accumulation, and its canopy spectral characteristics contain rich information on biochemical parameters and yield potential. This study explores the application advantages of combining multi-dimensional UAV remote sensing features with advanced machine learning algorithms for rice yield prediction at the full heading stage. Using a multispectral UAV to acquire images, this research systematically constructed six categories of feature sets, including red-green-blue (RGB), multispectral (MS), multispectral to hyperspectral (MSTPP), spatial texture (Texture), vegetation index (VI), and multi-dimensional fusion (Fusion). At the algorithmic level, eight machine learning algorithms were comprehensively evaluated, with a focus on comparing kernel methods represented by support vector regression (SVR) and various nonlinear ensemble models. The experimental results indicate that MS data exhibit outstanding prediction potential among single-dimensional features. The strategy of fusing spectral and texture information achieved excellent prediction stability, wherein the SVR model combined with multi-dimensional fusion features achieved an ideal fitting effect (coefficient of determination R²=0.828), calibrating the spectral signals impaired by canopy closure during the full heading stage, and effectively correcting the systematic estimation bias of single-dimensional models in extreme yield intervals. Furthermore, this experiment verified through the introduction of the Shapley additive explanations (SHAP) framework that the green normalized difference vegetation index (GNDVI) played a core driving role in yield prediction, objectively revealing the safeguard mechanism of population growth balance on the final yield. The approach of "multi-dimensional fusion combined with nonlinear algorithms and attribution analysis" explored in this study provides a feasible approach for constructing quantitative yield estimation models with both high robustness and agronomic interpretability.

The development of the low-altitude economy is reshaping the technological functions, organizational forms, and regulatory demands of agricultural aircraft. Represented by agricultural drones, such aircraft have evolved from seasonal plant protection equipment into low-altitude production units integrating field operations, crop and farmland sensing, disaster response, and socialized service provision. As application boundaries continue to expand, the risks associated with agricultural low-altitude operations have also shifted from conventional flight safety concerns to a more comprehensive governance problem in which flight safety, operational quality, environmental externalities, and liability traceability are mutually intertwined. Against this background, a comparative analytical framework was developed in this study centered on airspace accessibility, operational authorization, regulatory visibility, operational compliance, and enforcement traceability. And the governance regimes for agricultural low-altitude operations across major jurisdictions, including China, the United States, the European Union, Australia and New Zealand, were systematically reviewed. In addition, a regulatory release density indicator was introduced, together with publicly available data on registered operators, registered unmanned aircraft, and remote pilots, to enable comparison of the relationships between diverse regulatory supply forms and large-scale operational capacity. The findings showed that major jurisdictions have developed three broad governance pathways: a strong-foundation model, a high-efficiency authorization model, and a risk-assessment-and-service model. In agricultural scenarios, institutional effectiveness depends not on the sheer number of individual rules, but on whether airspace access, digital identification, operational oversight, and evidentiary record retention can be integrated into an executable and verifiable engineering closed loop. Moreover, the concurrent expansion of common application scenarios, including production operations, remote sensing monitoring, emergency support, and cross regional socialized services, has further intensified the demand for differentiated authorization, tiered regulation of payload release, and cross regional collaborative oversight. On this basis, the study proposed an engineering-oriented compliance pathway centered on mission-based classification, the coupling of authorization and traceable records, tiered control of payload release, and agriculture-oriented continuing airworthiness management. The results may provide a useful reference for improving Chinese governance system of the agricultural low-altitude economy and for promoting the standardized application of agricultural drones.

The deep integration of the low-altitude economy with agricultural and rural development is of strategic importance for enhancing comprehensive competitiveness, building a modern agricultural production system, and strengthening national food security. This study systematically reviews the application scenarios and existing challenges of low-altitude aircraft in the hilly and mountainous regions of Sichuan Province. It elaborates on the various low-altitude carrier platforms and their supporting power systems, as well as the intelligent control and management technology systems deployed in these regions. The study analyzes application scenarios including intelligent plant protection and precision pesticide application, low-altitude remote sensing and agricultural condition monitoring, transportation of agricultural inputs and products, seeding, fertilization, and pollination operations, as well as emergency support and logistics distribution. Furthermore, it identifies the issues and challenges faced by low-altitude carrier platforms in the hilly regions across three key aspects: equipment R&D and manufacturing, intelligent operational technologies, and operation and maintenance. From the perspective of critical technology breakthroughs, it proposes conducting integrated validation of models for full-process mechanized and intelligent systems alongside complete equipment sets. It also suggests developing a testing and validation method for “air-space-ground” integrated collaborative operation system for agricultural machinery in hilly regions. From the three dimensions of intelligence, greenness, and safety, it further clarifies the development requirements for low-altitude agricultural aviation equipment technology. From the perspective of optimizing the innovation ecosystem, it proposes improving mechanisms for talent introduction and cultivation, establishing a “platform support-talent guarantee-industry collaboration” trinity industry-university-research development system, and forming a virtuous-cycle innovation system for the low-altitude agricultural equipment industry. This approach aims to comprehensively enhance the technological level and industrial competitiveness of agricultural machinery in hilly regions. By establishing full-process mechanization demonstration zones in typical hilly regions such as Sichuan Province, conducting equipment validation and technology maturation, and strengthening training for grassroots operators, it provides directional guidance for developing replicable and scalable integrated solutions for agricultural mechanization in hilly regions.

As the low-altitude economy joins the ranks of national strategic emerging industries, the integration of low-altitude agricultural technology and equipment into agricultural production continues to deepen, serving as a cornerstone for promoting agricultural modernization and intelligent agriculture. This paper systematically evaluates the development status and future trends of low-altitude agricultural technology and equipment in China, focusing on policy frameworks, key technologies, and typical application scenarios. First, from the perspective of aerial platforms and intelligent control, the paper summarizes the current development of multi-rotor, single-rotor, and hybrid unmanned aerial vehicles (UAVs), and traces the evolution of key technologies in power systems, flight control algorithms, and navigation positioning. Concurrently, the progress of intelligent control technologies is outlined with an emphasis on low-altitude communication, cloud-based management, and multi-UAV coordination. Second, regarding low-altitude sensing and precision operations, the paper highlights the applications of UAV-based remote sensing in crop monitoring, alongside the technological pathways of precision spraying and broadcast sowing, identifying variable-rate operations and intelligent control as pivotal research frontiers. Finally, by examinging the inspection and evaluation systems and standardization frameworks of low-altitude agricultural equipment, the paper identifies existing challenges in operational specifications, quality evaluation, and systematic standards. The analysis reveals that although low-altitude agricultural technology offers significant advantages in operational efficiency and resource utilization, it remains constrained by insufficient equipment adaptability, limited operational stability in complex environments, inadequate multi-source information fusion and weak intelligent decision-making capabilities, and incomplete standard systems. Future research should prioritize multi-source sensing fusion, intelligent control, and data-driven decision-making technologies, while improving standard and evaluation systems, to promote the development of low-altitude agricultural technology toward precision, intelligence, and large-scale applications. This study provides a reference for research, equipment development, and policymaking in the field of low-altitude agriculture.

To address the degradation of power generation efficiency caused by soiling accumulation in photovoltaic (PV) power plants, as well as the localization challenges faced by cleaning unmanned aerial vehicles (UAVs) due to limitations in edge computing power and complex background interference, this paper proposes an edge-computed cleaning UAV solution based on the Jetson Orin Nano platform. Within the YOLO11n framework, a lightweight photovoltaic panel soiling detection algorithm designated as GOP-YOLO11n is developed. By introducing GroupDC structure, the C3k2 modules in the backbone network undergo lightweight reconstruction, minimizing computational overhead while enhancing the feature representation of small targets against complex backgrounds. Furthermore, an OptimizedDC multi-scale feature extraction module is embedded into the feature fusion network to improve the perception of multi-scale targets, such as tiny bird droppings and elongated water stains. In the detection head, an upsampling and feature aggregation block (UP-Block) is implemented to strengthen multi-pixel pattern aggregation. For the experiments, a visible-light image dataset of PV panels captured by UAVs, yielding 4 190 valid imagespartitioned into training, validation, and test sets at an 8：1：1 ratio, with input resolution standardized to 960 pixels × 960 pixels. Under identical training and testing configurations, GOP-YOLO11n achieved a Precision of 91.1%, a Recall of 85.8%, a mAP@50 of 92.5%, and a mAP@50：0.95 of 76.8%, outperforming the baseline YOLO11n model by 2.9, 3.4, 1.8, and 4.4 percentage points, respectively. The model parameters were reduced from 2.582 M to 2.068 M, with a floating-point operation count of 6.7×10⁹. Moreover, hardware deployment tests using TensorRT FP16 on the Jetson Orin Nano Super 8 G platform showed an inference speed of 114.7 f/s, a single-frame latency of 8.7 ms, and a GPU memory footprint of 1 685 MB. The results indicate that the proposed method effectively reconciles the need for high-accuracy multi-scale soiling detection in complex scenes with the constraints of lightweight edge-side real-time inference, thereby providing robust visual perception support for soiling recognition, localized cleaning strategies, and the intelligent operation and maintenance (O&M) of PV cleaning UAVs.

Unmanned aerial vehicle (UAV)-based fertilization is increasingly applied in rice production; however, fertilization prescriptions are still largely dependent on empirical knowledge and lack decision support based on real-time crop nutrient status. Meanwhile, limitations in hyperspectral data processing and model construction restrict the high-precision estimation of chlorophyll content in cold-region rice, thereby constraining the accurate implementation of variable-rate fertilization. To address these issues, this study combined multiple spectral preprocessing methods with feature variable selection techniques, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), iterative retention of informative variables (IRIV), and variable iterative space shrinkage approach (VISSA), to extract key spectral bands. Models including least squares support vector regression (LSSVR), particle swarm optimization-based LSSVR (PSO-LSSVR), partial least squares regression (PLSR), and a deep learning model (1DCNN-WN) were developed for chlorophyll content inversion. Based on the inversion results, nitrogen fertilization prescription maps were generated to guide UAV-based variable-rate fertilization at the tillering stage. The results showed that the SNV-IRIV-1DCNN-WN model achieved the highest inversion accuracy under the optimal preprocessing and feature selection combinations, with an R² of 0.687, which was 0.047, 0.031, and 0.027 higher than those of SNV-UVE-LSSVR, NL-VISSA-PSO-LSSVR, and MSC-IRIV-PLSR, respectively, while the RMSE was reduced by 0.281, 0.147, and 0.107. The coefficients of variation for no fertilization (N0), low fertilization (N1), standard fertilization (N2), and excessive fertilization (N3) treatments were 6.08%, 4.16%, 3.42%, and 3.07%, respectively, indicating good fertilization uniformity. These findings provide theoretical support and technical guidance for hyperspectral-based chlorophyll inversion and precision fertilization in cold-region rice production.

The leaf area index (LAI) is a key biophysical parameter characterizing the canopy structure and growth status of winter wheat, playing a crucial role in precision agriculture monitoring and crop condition assessment. However, existing inversion methods suffer from unstable accuracy and insufficient model generalization capabilities across varying flight altitudes. To adress these limitaions, this study develops an LAI estimation framework based on multi-altitude UAV multispectral data by integrating ensemble learning architectures with multiple machine learning algorithms. Analysis of variance (ANOVA) was applied to analyze the mechanisms through which flight altitude influences spectral reflectance and textural features. Following the extraction of vegetation and textural indices from the multispectral data, Pearson and Spearman correlation analyses were conducted to screen LAI-sensitive features for predictive modeling. Four distinct models—XGBoost, RF, XGBoost-EN, and RF-Ridge—were subsequently established to perform multi-altitude and multi-feature LAI inversions and comparative accuracy evaluations. The results indicate that UAV flight altitude exerts a significant scale effect on both reflectance and textural characteristics, with 80 m indentified as the critical threshold for reflectance variations. Regardless of whether single-feature or mixed-feature inputs were utilized, 20 m flight altitude consistently yielded the highest estimation accuracy across all tested altitudes, R² achieving a maximum of 0.745, RMSE of 0.699, and MAE of 0.507. Furthermore, The XGB-EN and RF-Ridge ensemble models outperformed the standalone machine learning models, exhibiting superior inversion performance with an R² of up to 0.802, an RMSE down to 0.592, and an MAE down to 0.421, indicating excellent fitting accuracy and stability. Overall, XGBoost-EN is the optimal model for the LAI inversion task. This study validated the feasibility of ensemble learning models and machine learning methods for high-precision LAI inversion across multiple altitudes, providing a theoretical foundation and technical pathway for remote sensing-driven crop growth monitoring.

Utilizing radiative transfer models to simulate spectral data for improving the robustness and interpretability of leaf area index (LAI) inversion models is a major focus in current research. However, the application of point cloud data, which can directly reflect the physical structure of rice, remains underdeveloped. To bridge this gap, this study proposes a rice LAI inversion method that integrates a three-dimensional radiative transfer model with unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) technology. Virtual rice canopy scenes were constructed using the LESS three-dimensional radiative transfer model to generate simulated point cloud data, while UAV LiDAR was employed to collect field-measured data. Pearson correlation analysis was used to select key features, leading to the construction of a feature subset comprising height features, vertical layer density features, and cumulative density features. Three machine learning models-ridge regression, support vector regression, and random forest-were trained and validated on simulated datasets, measured datasets, and a simulation-measurement hybrid dataset. The results show that the simulated data generated by the LESS model demonstrate strong consistency with measured point clouds in vertical distribution, confirming the validity of the simulation. Feature analysis indicates that the 90th percentile height (H90) and vegetation canopy volume (VCM) manifest the strongest correlations with LAI. Model comparison reveals that the random forest model performs best on the hybrid dataset, achieving a validation R² of 0.918 and RMSE of 0.787. The hybrid dataset strategy yields better fitting performance than using the measured dataset alone, effectively enhancing model generalization. In conclusion, the proposed method, which integrats three-dimensional radiative transfer modeling and UAV LiDAR technology, enables rapid and accurate estimation of rice LAI, providing a new technical pathway for precise monitoring of rice growth status and supporting high-throughput acquisition of crop phenotypic information within the framework of agricultural low-altitude economy.

Real-time and accurate monitoring of the winter wheat nitrogen nutrition index (NNI) is of great significance for optimizing field water and fertilizer management and reducing agricultural non-point source pollution. To address the issues of modal confusion in existing deep networks when processing multi-source UAV remote sensing data, as well as the loss of two-dimensional spatial topological information during temporal modeling, this study proposes a dual-branch multi-source feature fusion memory network (DMFM-Net). Multispectral images and canopy structural data of winter wheat at the tillering, jointing, and booting stages were collected by UAV. An enhanced CBAM-ResNet50 dual-branch backbone was employed to separately extract spectral features and canopy structural features. The spatially preserved feature tensors were then directly input into a convolutional long short-term memory (ConvLSTM) network for temporal modeling. The results show that: (1) Compared with the traditional serial network (CBAM-ResNet-LSTM) and the single-branch network (CBAM-ResNet-ConvLSTM), DMFM-Net effectively overcomes the noise interference of multimodal heterogeneous data and achieves the best inversion performance at all key growth stages. The coefficients of determination (R²) of the model at the tillering, jointing, and booting stages reach 0.76, 0.86, and 0.82, respectively, with root mean square errors (RMSE) as low as 0.11, 0.10, and 0.14. (2) Ablation experiments further confirm the necessity and scientific validity of the proposed architectural design. The ConvLSTM model lacking a deep spatial encoder shows a significant decrease in R² of 0.21-0.24. Moreover, compared with the static spatiotemporal feature extraction of 3D-ResNet, DMFM-Net, by virtue of its ConvLSTM module covering a long-range dynamic memory mechanism across the whole growth period, improves the R² of the inversion models at each stage by 0.07-0.16. (3) The field-scale spatial distribution maps generated by the optimal model accurately reconstruct the local spatial heterogeneity and temporal evolution patterns of winter wheat nitrogen nutritional status under different nitrogen application gradients. In summary, this study provides a new theoretical paradigm and technical support for multimodal time-series dynamic monitoring of crop nutritional status in complex field environments.