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Stereoscopic agriculture, as an advanced method of agricultural production, poses new challenges for multi-task trajectory planning of unmanned aerial vehicles (UAVs). To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture, a multi-task trajectory planning model and algorithm (IEP-AO) that synthesizes flight safety and flight efficiency is proposed. Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics, the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects, including the path, slope, altitude, corner, energy and obstacle threat, to improve the effectiveness of the trajectory planning model. And combined with the path optimization algorithm, an Aquila optimizer (IEP-AO) based on the interference-enhanced combination model is proposed, which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks, and jump out of the locally optimal trajectory path region timely, to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency. Meanwhile, four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene, and the experimental results can show that the proposed multi-task trajectory planning method can meet the multi-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) increases spectrum efficacy by permitting multiple devices to share link resources. It can be used to provide convenient offloading computing services for edge devices (EDs) in unmanned aerial vehicle (UAV) and mobile-edge computing (MEC) networks. However, due to the Line-of-Sight (LoS) of UAV transmission, NOMA-based UAV-MEC systems are susceptible to information eavesdropping. In this paper, we investigate a secure offloading model for a NOMA-based UAV-aided MEC in Internet of Things (IoT) network concerning an aerial eavesdropper (EAV) that considers imperfect channel state information (ipCSI) and imperfect successive interference cancellation (ipSIC). We derive the expression of secrecy successful computation probability (SSCP) across the entire system to analyze EAV's impact on the performance of the NOMA-based UAV-aided MEC in IoT networks. In addition, we present a formulation of an optimization problem that optimizes the SSCP through the optimization of the UAV's altitude and location, as well as the offloading ratio. To address this issue, a genetic algorithm (GA)-based approach was implemented. The results of our study were corroborated by the Monte Carlo simulations, which assessed system performance by considering multiple system parameters including the UAV's location, altitude, average transmit signal-to-noise ratio (SNR), and offloading ratio. [ABSTRACT FROM AUTHOR]

Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage (Brassica rapa L. ssp. pekinensis) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments. [ABSTRACT FROM AUTHOR]

Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading. CER-MADDPG emphasizes collaboration between UAVs and uses historical UAV experiences to classify and obtain optimal strategies. It enables collaboration among edge devices through the design of the 'critic' network. Additionally, by defining good and bad experiences for UAVs, experiences are classified into two separate buffers, allowing UAVs to learn from them, seek benefits, avoid harm, and reduce system overhead. The performance of CER-MADDPG was verified through simulations in two aspects. First, the influence of key hyperparameters on performance was examined, and the optimal values were determined. Second, CER-MADDPG was compared with other baseline algorithms. The results show that compared with MADDPG and stochastic game-based resource allocation with prioritized experience replay, CER-MADDPG achieves the lowest system overhead and superior stability and scalability. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can collect earth surface information with synthetic aperture radar (SAR) sensors during night, day, and bad weather. However, UAVs have only limited volume and weight for the SAR payload. A very light dual circularly polarized pyramid horn antenna was developed in order to improve sense capability in UAV dual polarimetry SAR images. The pyramid horn was designed and simulated in computer simulation technology (CST) 2022. The horn was capable of producing two circular polarizations right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) alternately using a three stepped septum polarizer and coaxial probe feed on two sides of the waveguide. The results show that the horn antenna was capable of having a wide S11 and S22 bandwidth from 4 GHz to 6.7 GHz. The horn was capable of producing two circular polarizations with an axial ratio below 3 dB from 4.4 GHz to 6.4 GHz. This gain horn antenna was relatively stable in the range of 15 dBiC with a reasonably narrow 3 dB beamwidth of 28° both RHCP and LHCP polarisation in E and H fields respectively. [ABSTRACT FROM AUTHOR]

Experimental studies on the application of unmanned aerial vehicles (UAVs) to extinguish high-repeatability transmission line wildfires are not available. In this study, a scheme involving a UAV releasing a fire-extinguishing bomb at a high altitude for firefighting was proposed, and a simulated fire-extinguishing experimental platform was constructed to simulate a UAV releasing a fire-extinguishing bomb. In addition, the characteristics of water-based fire-extinguishing bombs of 5, 10, 20, and 50 kg as well as dry powder bombs of 25, 30, and 50 kg were investigated by changing parameters such as the type and mass of the fire-extinguishing agent carried by the bombs. It was noted that a 30 kg or more dry powder fire-extinguishing bomb could extinguish a fire in four 1A cribs at one time. The diffusion area of the fire-extinguishing medium was obtained from the perspective of the UAV. The diffusion area was 45–90 m2 for the water-based fire-extinguishing bomb and 130–700 m2 for the dry powder bomb. As calculated from the area of the fire scene extinguished by each fire-extinguishing bomb per unit mass, the utilization rate of a fire-extinguishing medium was highest with the 30 kg fire-extinguishing bomb, followed by the 50 kg bomb. In the high wildfire incidence period during the Qingming Festival in 2024, a UAV was used to release fire-extinguishing bombs to extinguish an incipient wildfire near a transmission line at a Hunan Power Grid site. [ABSTRACT FROM AUTHOR]

The effective planning of UAV trajectories in a 3D environment presents a complex global optimization challenge that must account for numerous constraints, including urban settings, mountainous terrain, obstacles, no-fly zones, flight boundaries, travel distances, and trajectory change rates. This paper addresses the limitations of the whale optimization algorithm in 3D trajectory planning—specifically its slow convergence, low accuracy, and susceptibility to local optimum—by proposing an improved whale optimization algorithm. This enhancement incorporates an inverse learning mechanism to increase the diversity of the initial population and integrates a nonlinear convergence factor with a random number generation mechanism to optimize the balance between global and local search capabilities. Our findings indicate that for both the standard and improved whale optimization algorithms, each individual in the population represents a feasible solution, corresponding one-to-one with distributed trajectories in the search space. Given that route planning typically occurs in three dimensions, there is spatial symmetry among the multiple potential trajectories from the starting point to the endpoint. The optimization algorithm identifies the optimal solution by exploring these symmetric trajectory paths, ultimately selecting the most favorable one based on additional constraints (e.g., no-fly zones and fuel consumption). Moreover, the convergence of the whale optimization algorithm depends on the diversity of individuals in the population and the thorough exploration of the search space. This symmetry facilitates a more uniform exploration of various trajectories by the population. In some instances, the optimization algorithm has achieved a 7.00% improvement in fitness value, a 10.05% reduction in optimal distance, and a 28.73% decrease in standard deviation. The increase in optimal values and the decrease in worst-case values underscore the effectiveness of the optimization algorithm, while the reduction in standard deviation reflects the stability of the algorithm's output data. These results further confirm the advantages of the optimized algorithm. [ABSTRACT FROM AUTHOR]

Tropical forests have high species richness, being considered the most diverse and complex ecosystems in the world. Research on the variation and maintenance of biodiversity in these ecosystems is important for establishing conservation strategies. The main objective of this study was to test the Spectral Variation Hypothesis through associations between species diversity and richness measured in the field and hyperspectral data collected by a Remotely Piloted Aircraft (RPA) in areas with secondary tropical forest in the Brazilian Atlantic Forest biome. Specific objectives were to determine which dispersion measurements, standard deviation (SD) or coefficient of variation (CV), estimated for the n pixels occurring within each sampling unit, better explains species diversity; the effects of pixel size on the direction and intensity of this relationship; and the effects of shaded pixels within each sampling unit. The spectral variability hypothesis was confirmed for the Atlantic Forest biome, with R2 of 0.83 for species richness and 0.76 and 0.69 for the Shannon and Simpson diversity indices, respectively, using 1.0 m illuminated pixels. The dispersion (CV and SD) of hyperspectral bands were most strongly correlated with taxonomic diversity and richness in the red-edge and near-infrared (NIR) regions of the electromagnetic spectrum. Pixel size affected R2 values, which were higher for 1.0 m pixels (0.83) and lower for 10.0 m pixels (0.71). Additionally, illuminated pixels had higher R2 values than those under shadow effects. The main dispersion variables selected as metrics for regression models were mean CV, CV for the 726.7 nm band, and SD for the 742.3 and 933.4 nm bands. Our results suggest that spectral diversity can serve as a proxy for species diversity in the Atlantic Forest. However, factors that can affect this relationship, such as taxonomic and spectral diversity metrics used, pixel size, and shadow effects in images, should be considered. [ABSTRACT FROM AUTHOR]

With the advancement of wireless communication technology, the number of wireless network terminals has exploded, and various new business scenarios have emerged. The 6G mobile communication technology not only surpasses 5G standards in terms of transmission rate, delay, power and other performances, but also extends the communication range to multiple fields such as air, ground, ocean, etc., which greatly promotes Unmanned Aerial Vehicle (UAV) communication technology research and development. Compared to terrestrial networks, UAV communication has advantages such as high flexibility and easy deployment. However, there are still many problems and challenges in practical applications. In this paper, we will first introduce the functions and application scenarios of UAV communication, then discuss the current challenges and related technical research, and finally look forward to the future development prospects. [ABSTRACT FROM AUTHOR]

The combination of reconfigurable intelligent surface (RIS) with the unmanned aerial vehicle (UAV) and the non-orthogonal multiple access (NOMA) has emerged as a promising technology in the sixth generation (6G) network. However, UAV downlinks are susceptible to potential eavesdropping attacks due to the open and broadcasting nature of wireless channels. In addition, transmitting information with infinite blocklength (IBL) is impractical in 6G applications. In this paper, we propose a secure RIS-assisted UAV communication system based on NOMA with finite blocklength (FBL) transmission. To maximize the average secrecy rate for all ground users under the mobility and power constraints of the UAV, we jointly optimize the phase shift of the RIS, the trajectory, the transmit power of the UAV, and the user scheduling. To solve the formulated non-convex problem, we first transform the optimization problem into four convex sub-problems, i.e., phase shift optimization, trajectory optimization, transmit power optimization, and user scheduling optimization. Then, an iterative algorithm is developed based on the successive convex approximation (SCA) to solve the four sub-problems. Numerical results show that the average secrecy rate for all ground users achieved with the proposed algorithm is higher than that achieved with the traditional algorithms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) photogrammetry is a technology used to create highly accurate maps, and the real-time kinematic (RTK) global navigation satellite system (GNSS) network has been implemented by several organizations to support their missions. Recent advancements in computer vision have driven progress in photogrammetry, alongside the development of more accessible UAVs equipped with GNSS technology. This paper evaluates the accuracy of UAV imagery and processing using ground control points (GCPs) with coordinates from the Thailand RTK GNSS network. The study area is located on the Sathing-Phra Peninsula in Songkhla Province, southern Thailand, covering approximately 174 hectares with a total of 660 images. The average ground sampling distance (GSD) was around 4.35 cm. The processed photogrammetry system used 8 GCPs via the GNSS RTK network, and results were compared with 25 checkpoints from a static survey. The UAV photogrammetry results showed a horizontal accuracy for orthomosaics and vertical accuracy for the digital surface model (DSM), with CE 95 values of 6.99 cm and 12.44 cm, respectively, as per the NSSDA standard. These results comply with the Thailand UAV Surveying for Engineering (TUSE) standards. According to the 2014 American Society for Photogrammetry and Remote Sensing (ASPRS) standards, horizontal accuracy class I and vertical accuracy class II should be applied. The map scale factor (MSF) standard was 1:160, with a contour interval of 20 cm. Additionally, the orthomosaic DSM can be imported into open-source programs for use in a web GIS online system, enabling local agencies to retrieve and utilize the data. [ABSTRACT FROM AUTHOR]

Background: The rise in natural and man-made disasters has increased the need for effective search-and-rescue tools, particularly in resource-limited areas. Unmanned Aerial Vehicles (UAVs) are increasingly used for this purpose due to their flexibility and lower operational costs. However, finding the most efficient paths for these UAVs remains a challenge, as it is essential to maximize victim location and minimize mission time. Methods: This study presents an autonomous UAV-based approach for identifying victims, prioritizing high-risk areas and those needing urgent medical attention. Unlike other methods focused solely on minimizing mission time, this approach emphasizes high-risk zones and potential secondary disaster areas. Using a partially observable Markov decision process, it simulates victim detection through an image classification algorithm, enabling efficient and independent operation. Results: Experiments with real data indicate that this approach reduces risk by 66% during the mission's first half while autonomously identifying victims without human intervention. Conclusions: This study demonstrates the capability of autonomous UAV systems to improve search-and-rescue efforts in disaster-prone, resource-constrained regions by effectively prioritizing high-risk areas, thereby reducing mission risk and improving response efficiency. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its potential applications. These systems offer enhanced situational awareness and operational efficiency, enabling complex tasks that are beyond the capabilities of individual systems by leveraging the complementary strengths of UAVs and UGVs. Key areas explored in this review include multi-UAV and multi-UGV systems, collaborative aerial and ground operations, and the communication and coordination mechanisms that support these collaborative efforts. Furthermore, this paper discusses potential limitations, challenges and future research directions, and considers issues such as computational constraints, communication network instability, and environmental adaptability. The review also provides a detailed analysis of how these issues impact the effectiveness of UAV-UGV collaboration. [ABSTRACT FROM AUTHOR]

Object detection in aerial images poses significant challenges due to the high dimensions of the images, requiring efficient handling and resizing to fit object detection models. The image-slicing approach for object detection in aerial images can increase detection accuracy by eliminating pixel loss in high-resolution image data. However, determining the proper dimensions to slice is essential for the integrity of the objects and their learning by the model. This study presents an evaluation of the image-slicing approach for alternative sizes of images to optimize efficiency. For this purpose, a dataset of high-resolution images collected with Unmanned Aerial Vehicles (UAV) has been used. The experiments evaluated using alternative YOLO architectures like YOLOv7, YOLOv8, and YOLOv9 show that the image dimensions significantly change the performance results. According to the experiments, the best mAP@05 accuracy was obtained by slicing 1280 × 1280 for YOLOv7 producing 88.2. Results show that edge-related objects are better preserved as the overlap and slicing sizes increase, resulting in improved model performance. [ABSTRACT FROM AUTHOR]

In the process of the collaborative work of Unmanned Aerial Vehicle (UAV) clusters, the cluster communication node test is often carried out by a single-node test, which leads to poor topology and robustness of the overall network system, an imbalanced communication network load, and high complexity of the communication test, which seriously affects the diversified needs of current users and the efficiency of large-scale task processing. To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. The system management node is used to monitor the status of the UAV testing task execution node and bandwidth resources. By taking advantage of the method of continuous interaction between the agent and the environment, the future state of the node after processing the current task to be assigned is predicted and evaluated from the perspective of interpretability, so as to achieve the effectiveness and stability of the UAV cluster testing task collaborative execution. The experimental results show that our proposed method can ensure the stable operation of the UAV cluster, accurately predict the future state, and reduce the load degree and bandwidth resource consumption of the large-scale test task network system. [ABSTRACT FROM AUTHOR]

This paper introduces a stable adaptive PID-like control scheme for quadrotor Unmanned Aerial Vehicle (UAV) systems. The PID-like controller is designed to closely estimate an ideal controller to meet specific control objectives, with its gains being dynamically adjusted through a stable adaptation process. The adaptation process aims to reduce the discrepancy between the ideal controller and the PID-like controller in use. This method is considered model-free, as it does not require knowledge of the system's mathematical model. The stability analysis performed using a Lyapunov method demonstrates that every signal in the closed-loop system is Uniformly Ultimately Bounded (UUB). The effectiveness of the proposed PID-like controller is validated through simulations on a quadrotor for path following, ensuring accurate monitoring of the target positions and yaw angle. Simulation results highlight the performance of this control scheme. [ABSTRACT FROM AUTHOR]

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay. [ABSTRACT FROM AUTHOR]

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies. [ABSTRACT FROM AUTHOR]

The aerial transportation system belongs to a symmetrical system and has recently garnered increasing attention from researchers due to its broad application range and convenient operation. The control difficulty of the aerial transportation system lies in the fact that the load is not directly actuated, posing a significant challenge for state-constrained control. Taking the motion of an unmanned aerial vehicle (UAV) suspension transportation system within complex pipelines as an example, this paper employs the the swept volume signed distance field (SVSDF) method to search for state boundaries, which is an aspect not considered or elaborated in many state-constrained control approaches. Furthermore, adaptive state-constrained control based on the radial basis function (RBF) neural network is utilized for the case of experiencing unknown air resistance. The convergence of the proposed method for underactuated and actuated state variables is theoretically demonstrated based on the Lyapunov technique. Compared with existing methods, the error integral index demonstrates that the proposed method displays better convergence capability in the simulation section when considering state constraints under disturbance and air resistance. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a direct indicator of crop canopy growth and serves as an indirect measure of crop yield. Unmanned aerial vehicles (UAVs) offer rapid collection of crop phenotypic data across multiple time points, providing crucial insights into the evolving dynamics of the LAI essential for crop breeding. In this study, the variation process of the maize LAI was investigated across two locations (XD and KZ) using a multispectral sensor mounted on a UAV. During a field trial involving 399 maize inbred lines, LAI measurements were obtained at both locations using a random forest model based on 28 variables extracted from multispectral imagery. These findings indicate that the vegetation index computed by the near-infrared band and red edge significantly influences the accuracy of the LAI prediction. However, a prediction model relying solely on data from a single observation period exhibits instability (R2 = 0.34–0.94, RMSE = 0.02–0.25). When applied to the entire growth period, the models trained using all data achieved a robust prediction of the LAI (R2 = 0.79–0.86, RMSE = 0.12–0.18). Although the primary variation patterns of the maize LAI were similar across the two fields, environmental disparities changed the variation categories of the maize LAI. The primary factor contributing to the difference in the LAI between KZ and XD lies in soil nutrients associated with carbon and nitrogen in the upper soil. Overall, this study demonstrated that UAV-based time-series phenotypic data offers valuable insight into phenotypic variation, thereby enhancing the application of UAVs in crop breeding. [ABSTRACT FROM AUTHOR]

Blast disease has been one of the most serious threats to rice production. However, the conventional evaluation of rice blast resistance cannot fully meet the large-scale production in recent years. In this study, an efficient evaluation was proposed for rice neck blast resistance via combining unmanned aerial vehicle (UAV) low-altitude remote sensing with the YOLOv7 model. 2 565 images of rice neck blast were collected using UAV and then divided into small-sized images (≤ 1240×1240 pixels) in the annotated area. The small-sized images were further subjected to a random combination of five operations, including rotation, scaling, translation, cropping, and changing contrast. The images with low resolution were removed after data cleaning. Finally, the dataset size was expanded with better diversity. The squeeze extinction attention and deformable convolution were introduced into the YOLOv7 model, in order to capture the fine-grain features of the rice neck blast disease spot. YOLOv7_Neckblast model was established for the rice neck blast detection. The number of affected ears was obtained for 15 rice varieties. The incidence rate was calculated for the disease grade of rice neck blast. Among them, 4, 4, 3, 5, 7, and 9 rice varieties of grades 1, 3, 5, and 7, as well as 9, 2, and 2 were assessed by YOLOv7_Neckblast, respectively. At the intersection over the union (IoU) threshold of 0.5, the mean average precision (mAP) of YOLOv7_Neckblast for rice neck blast was 66.4%, which was 4.0, 6.4, and 5.8 percentage points higher than that of the original YOLOv7, FCOS (fully convolutional one-stage object detection), and RetinaNet models, respectively. The recall rate was 75%, which was 4.0, 8.0, and 22.0 percentage points higher than those of the three models, respectively. The F1 score was 71%, which was 4.0, 5.0, and 12.0 percentage points higher than those of the three models, respectively. The training YOLOv7_Neckblast was relatively stable with the low floating-point operations per second (FLOP) under the same training conditions. The loss values remained stable with about 0.019 and 0.020 at the end of training, which was lower than those of FCOS and RetinaNet models. Furthermore, the molecular-assisted marker selection (MAS) showed that the contribution of the Pit and Pib genes to the resistance to the neck blast was 100.0% and 57.14%, respectively. The rice varieties carrying the Pit and Pib genes also exhibited a stronger resistance to disease. In addition, YOLOv7_Neckblast achieved an average accuracy of 86.67% in evaluating 15 rice varieties' resistance, compared with the actual resistance level. The low-altitude UAV remote sensing coupled with machine learning can be used to evaluate the resistance to rice neck blast for rice breeding. [ABSTRACT FROM AUTHOR]

The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model's parameter count and computational load by constructing the unite ghost and depth-wise separable convolution (UGDConv) series of lightweight convolutions and a lightweight detection head. Based on this, we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments. In particular, we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV's obstacle avoidance capability. To verify the effectiveness of the algorithm, the UAV obstacle detection (UAD) dataset was created. The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4% and reduces GFLOPs by 34.5% compared to YOLOv8n while reducing the number of parameters by 77.4% and the model size by 73%. These improvements significantly enhance the UAV's obstacle avoidance performance in complex environments, demonstrating its wide range of applications. [ABSTRACT FROM AUTHOR]

In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60–200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability. [ABSTRACT FROM AUTHOR]

Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R2 of 0.848, RMSE of 1.454 μg/cm2 and MAE of 1.121 μg/cm2. Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks. [ABSTRACT FROM AUTHOR]

Oil palm is highly valuable in tropical regions like Southeast Asia, including Indonesia. Therefore, accurate monitoring of oil palm trees is necessary for operational efficiency and reducing its environmental impact. Geospatial data, such as orthomosaic imagery from the unmanned aerial vehicle (UAV), can facilitate this goal. This research aims to integrate UAV data with deep learning algorithms, specifically Mask region-based convolutional neural network (R-CNN), to detect oil palm trees in Indonesia. We utilized Resnet-50 as the backbone and trained the model using data sampled from the template matching tool in eCognition. Considering factors like cloud shadows and other features, such as other plants, buildings, and road segments, we divided the study area into three containing different feature combinations in each. The Mask R-CNN model achieved an accuracy exceeding 80%, which is sufficient and makes it suitable for large-scale oil palm tree detection using high resolution images from UAV. [ABSTRACT FROM AUTHOR]

Timely and accurate mastery of the spatial and temporal development process of landslides is crucial for emergency rescue work. In the early morning of July 5, 2022, under the influence of continuous heavy rainfall, the left embankment slope of a highway in Guangdong Province, slipped as a whole, and a truck fell and was buried, resulting in the interruption of highway traffic. In order to accurately carry out landslide analysis and management, and to provide information support for emergency rescue and relief work, whole-process survey and analysis of the landslide area was carried out based on unmanned aerial vehicle tilt photography, 3D reconstruction and spatial analysis technology. In situations where traditional surveying and mapping work is difficult to carry out, it is possible to accurately grasp the whole process of the development of landslides in space and time under the circumstances and construct corresponding realistic three-dimensional models to provide effective reference information for on-site rescue and relief work. At the same time, through the comprehensive analysis of the acquired information, it is inferred that the landslide is a traction landslide under the effect of continuous rainfall. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with a miniaturized sensor package were developed for aerial observations, which realizes aerial observations affordable to scientists in atmospheric science and achieves aerial measurements in high spatial resolution. UAVs are deployed to a variety of aerial detecting tasks in different scientific scenarios including chemical industry parks (CIPs) with hazardous gases emissions, and some places difficult for humans to reach. In this study, UAV sensing technology was deployed to detect air pollutants in a suburb, a CIP and a natural gas plant, respectively. The effects of atmospheric conditions such as the atmospheric boundary layer height, long-distance transport and atmospheric stability on the spatiotemporal variations of the air pollutants vertical profiles were investigated by the UAV. The UAV with the sensor package was deployed to capture the methane (CH4) leakages in a natural gas plant. The spatiotemporal variations of CH4 in both vertical and horizontal directions studied by UAV were employed to calculate accurate CH4 emissions, which is crucial to reducing the emissions of greenhouse gases. The low-cost UAV sensing technology for air pollutants was developed by Dr. Xiaobing Pang, who was funded by the Newton Fellowship in 2009 and worked in the University of York. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

Fusarium crown rot (FCR) is a chronic disease in many regions of the world in wheat, caused by Fusarium culmorum, Fusarium pseudograminearum, and Fusarium graminearum. The operational efficacy of pesticide applications using unmanned aerial vehicles (UAVs) significantly affects the biological efficacy of the pesticides. This study aimed to compare the effectiveness of unmanned aerial vehicle and field sprayer applications in controlling crown rot diseases frequently observed in wheat crops in the Thrace region, Turkey. A licensed fungicide containing the active ingredients, prochloraz plus trifloxystrobin plus cyproconazole mixture was applied to wheat during the ZGS 27 growth stage. The disease severity, disease incidence, and the effectiveness of fungicide treatment on disease severity (%) were evaluated for F. culmorum crown rot disease. The results showed that the severity of the disease during the seedling stage was 11.25% and 18.33% for unmanned aerial vehicle and field sprayer applications, respectively. In the harvest stage, the incidence of disease was 28.33%-39.99% and 48.75%-51.25%, respectively, and the effectiveness of unmanned aerial vehicle application was found to be high, approximately 52%, during the seedling and harvest stages. The unmanned aerial vehicle, acting similarly to the field sprayer, exhibited higher grain quality under conditions of stress from disease. Furthermore, spike weight, grain weight, and number of grains exhibited stronger positive correlations compared to unmanned aerial vehicle treatment. Therefore, unmanned aerial vehicles have promising potential as viable options to manage FCR when the prevailing environmental conditions are not conducive to the use of field sprayer. The results of this research will guide future studies to investigate the efficacy of UAVs on a wider range of pesticides and to further develop the technology to investigate its effectiveness, cost-effectiveness, and sustainability in agricultural applications. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost‐ and time‐efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone‐based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent‐based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems. [ABSTRACT FROM AUTHOR]

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI – Green Optimized Soil Adjusted Vegetation Index, MCARI2 – Modified Chlorophyll Absorption Index-Improved, NDRE – Normalized Difference Red Edge, NDVI – Normalized Difference Vegetation Index, and OSAVI – Optimized Soil Adjusted Vegetation Index and WDVI – Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error – MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy's variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) communication has been proposed as an effective solution in both military and civilian scenarios, with low cost, high efficiency, high flexibility, and on‐demand deployment. Network simulation is an economically efficient method for validating new ideas in UAV communication. While some simulation tools have been proposed for UAV communications, there is a lack of a state‐of‐the‐art review to guide newcomers in this research field. In existing surveys, the discussion of network simulators is not comprehensive and the content discussed is outdated. There has also been no discussion of open‐source UAV simulation tools. To fill these research gaps, this survey presents an updated and comprehensive survey of UAV network simulation tools, including a unique collection of open‐source tools. Research challenges and opportunities have been proposed to inspire follow‐up studies. [ABSTRACT FROM AUTHOR]

In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection. With regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering, multi-UAV mission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstream schemes are tested, and the results show that the present method has a huge advantage in terms of space and time complexity, and at the same time, the accuracy for data extraction is relatively improved by 10.46% and 12.71%. Finally, by eliminating the clustering process and the subtask assignment process, the AoI performance decreases by 3.46× and 4.45×, and the energy performance decreases by 3.52× and 4.47×. This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations. [ABSTRACT FROM AUTHOR]

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks. [ABSTRACT FROM AUTHOR]

In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions. [ABSTRACT FROM AUTHOR]

Building instance extraction and recognition (BEAR) extracts and further recognizes building instances in unmanned aerial vehicle (UAV) images, holds with paramount importance in urban understanding applications. To address this challenge, we propose a unified network, BEAR-Former. Given the difficulty of building instance recognition due to the small area and multiple instances in UAV images, we developed a novel multi-view learning method, Cross-Mixer. This method constructs a cross-regional branch and an intra-regional branch to, respectively, extract the global context dependencies and local spatial structural details of buildings. In the cross-regional branch, we cleverly employed cross-attention and polar coordinate relative position encoding to learn more discriminative features. To solve the BEAR problem end to end, we designed a channel group and fusion module (CGFM) as a shared encoder. The CGFM includes a channel group encoder layer to independently extract features and a channel fusion module to dig out the complementary information for multiple tasks. Additionally, an RoI enhancement strategy was designed to improve model performance. Finally, we introduced a new metric, Recall@(K, iou), to evaluate the performance of the BEAR task. Experimental results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir's secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions. [ABSTRACT FROM AUTHOR]

We consider the issues of calibration of the transmission coefficient of an antenna system of the ground station used for monitoring of the energy characteristics of signals from global navigation satellite systems. We present the most commonly used method intended for calibration according to the solar radio-emission of the transmission coefficient of large-aperture reflector antenna systems used as basic in the construction of ground control stations. We describe the advantages and disadvantages of the method of calibration according to the radio-emission. To eliminate the disadvantages of calibration based on solar radio-emission (the limited range of working elevation angles and the necessity of using third-party data on the spectral intensity of solar radio-emission), it is proposed to consider an alternative approach, namely, the calibration based on the use of an artificial source of radio-emission placed on board of an unmanned aerial vehicle. The specific features of this approach, namely, the necessity of operation in the near-field zone of the investigated antenna system and the motion of an artificial source of radio-emission in a plane parallel to the aperture plane of the antenna system, are indicated. We develop a block diagram of the payload of an unmanned aerial vehicle for measuring in the near-field zone of the analyzed antenna system. We present the results of investigations of the characteristics of a payload signal generator of an unmanned aerial vehicle. We propose a solution, which eliminates the error in specifying the output power of the signal generator (the difference between the nominal and actual signal power) according to the results of measurement. The necessity of using a control signal power meter and a bandpass filter as components of the payload is demonstrated. A system for monitoring of the output power of signal generator is designed. The components of the payload satisfying the restrictions imposed on the weight and size characteristics are selected. The possibility of remote connection of an operator to the payload equipment of an unmanned aerial vehicle is realized. We also describe the prospects of subsequent development of the payload model. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) safety inspection is a developing technology that offers the benefits of high efficiency, low cost, and freedom from dangerous areas and unique situations. An urgent fundamental issue in the deployment of UAV in factories is how to successfully strike a balance between the effectiveness and cost of UAV safety inspection. In view of this, we build a route planning model for UAV inspection. And then, by using the path planning of UAV safety inspection as the research object, based on the two important evaluation indicators of cost and efficiency, we exploit fuzzy time window and adaptive genetic algorithms to design the solution algorithm. Finally, a case verifies the applicability and logic of the proposed model. The results show that the proposed path optimization model with fuzzy time window can reasonably pass all inspection points under balanced conditions, and the hybrid genetic algorithm has good optimization ability. [ABSTRACT FROM AUTHOR]

Defect identification of transmission lines has become a crucial step in ensuring the proper functioning of the current transmission system due to the ongoing growth of the power grid size. The study primarily concerns itself with the current shortcomings of unmanned aerial vehicle transmission defect detection, particularly in terms of image quality and other related issues. In response, an unmanned aerial vehicle transmission defect detection system based on edge computing has been proposed. This system employs edge computing networks and lightweight improvements, and finally, through the analysis of experimental data, the performance and detection effectiveness of the system are validated. The outcomes revealed that the accuracy of the model used for the study in detecting insulators is 0.05 higher than other models. The system was more effective in detecting normal insulators and abnormal insulators. The error of the system in detecting transmission line images was 0.18 lower than other algorithms, and the average percentage error was 0.20 lower compared to other model error values. This reveals that the system used in the study was able to improve the detection of transmission lines, and also improved the quality of the detected images. This is an excellent manual for enhancing UAV transmission line defect detection precision in the future. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are currently employed to carry different types of cargoes, such as medical products. Several advantages can be related to the integration of UAVs in health care systems, including the possibility to access remote areas, low costs and high mobility and speed. However, some concerns can arise regarding the payload integrity, especially considering medical products that can be sensitive to vibration and lose their therapeutic effect. This paper presents the flight dynamics of a quadrotor and an attached payload, assuming a flexible attachment between them. Constraint vector representation is used to model the flexible attachment and guarantee a physical distance between them. A dual loop control, formed by a sliding mode control and reduced dimension observer, is developed to improve the trajectory tracking and payload undesired oscillations. The estimated disturbance (DE) is then calculated by the difference between the estimated payload and the desired trajectories. Numerical results have shown that with the use of the DE strategy, the undesired oscillations are attenuated, showing a reduction from maximum peaks of 0.2 m to 0.05 m. Regarding performance index evaluation, a reduction of approximately 84% is observed in terms of payload oscillation. In a second case, with a different payload and external disturbance intensity, the proposed strategy is also able to positively estimate the payload vibration and, consequently, attenuate the undesired oscillation, with an 85% reduction. Therefore, the dual loop control represents an efficient strategy for tracking trajectory with low undesired oscillation intensity. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm's rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system's robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model's high discriminative ability. The results also indicate that the model meets the real-time requirements. [ABSTRACT FROM AUTHOR]

Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment. NEW & NOTEWORTHY: In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

The analysis of utilising unmanned aerial vehicles (UAVs) to form flying networks in obstacle conditions and various algorithms for obstacle avoidance is conducted. A planning scheme for deploying a flying LiFi network based on UAVs in a production facility with obstacles is developed and described. Such networks are necessary to ensure reliable data transmission from sensors or other sources of information located in dangerous or hard-to-reach places to the crisis centre. Based on the planning scheme, the following stages are described: (1) laying the LiFi signal propagation route in conditions of interference, (2) placement of the UAV at the specified points of the laid route for the deployment of the LiFi network, and (3) ensuring the reliability of the deployed LiFi network. Strategies for deploying UAVs from a stationary depot to form a flying LiFi network in a room with obstacles are considered, namely the strategy of the first point for the route, the strategy of radial movement, and the strategy of the middle point for the route. Methods for ensuring the uninterrupted functioning of the flying LiFi network with the required level of reliability within a given time are developed and discussed. To implement the planning stages for deploying the UAV flying LiFi network in a production facility with obstacles, the "Simulation Way" and "Reliability Level" software tools are developed and described. Examples of utilising the proposed software tools are given. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have become an integral part of modern life, serving both civilian and military applications across various sectors. However, existing power supply systems, such as batteries, often fail to provide stable, long-duration flights, limiting their applications. Previous studies have primarily focused on battery-based power, which offers limited flight endurance due to lower energy densities and higher system mass. Proton exchange membrane (PEM) fuel cells present a promising alternative, providing high power and efficiency without noise, vibrations, or greenhouse gas emissions. Due to hydrogen's high specific energy, which is substantially higher than that of combustion engines and battery-based alternatives, UAV operational time can be significantly extended. This paper investigates the potential of PEM fuel cells as an alternative power source for electric propulsion in UAVs. This study introduces an adaptive, fully functioning PEM fuel cell model, developed using a reduced-order modeling approach and optimized for UAV applications. This research demonstrates that PEM fuel cells can effectively double the flight endurance of UAVs compared to traditional battery systems, achieving energy densities of around 1700 Wh/kg versus 150–250 Wh/kg for batteries. Despite a slight increase in system mass, fuel cells enable significantly longer UAV operations. The scope of this study encompasses the comparison of battery-based and fuel cell-based propulsion systems in terms of power, mass, and flight endurance. This paper identifies the limitations and optimal applications for fuel cells, providing strong evidence for their use in UAVs where extended flight time and efficiency are critical. [ABSTRACT FROM AUTHOR]

The urban canopy refers to the spatial area at the average height range of urban structures. The light environment of the urban canopy not only influences the ecological conditions of the canopy layer region but also serves as an indicator of the upward light influx of artificial nighttime light in the urban environment. Previous research on urban nighttime light environment mainly focused on the urban surface layer and urban night sky layer, lacking attention to the urban canopy layer. This study observes the urban canopy layer with the flight and photography functions of an unmanned aerial vehicle (UAV) and combines color band remote sensing data with ground measurement data to explore the relationship between the three levels of the urban nighttime light environment. Furthermore, a three–dimensional observation method is established for urban nighttime light environments based on a combination of three observation methods. The research results indicate that there is a good correlation between drone aerial photography data and remote sensing data (R2 = 0.717), as well as between ground–measured data and remote sensing data (R2 = 0.876). It also shows that UAV images can serve as a new path for the observation of urban canopy nighttime light environments because of the accuracy and reliability of UAV aerial data. Meanwhile, the combination of UAV photography, ground measurement, and remote sensing data provides a new method for the monitoring and control of urban nighttime light pollution. [ABSTRACT FROM AUTHOR]

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields. [ABSTRACT FROM AUTHOR]