Pollination is the first step in the plant\'s fruit development. Therefore, fruit setting does not occur without pollination. Some problems encountered in natural pollination cause pollination not to be achieved as desired and cause significant losses in yield and fruit quality. Artificial pollination applications with drones are the best way to solve these problems. In this study, the AirPoll artificial pollination machine, which performs artificial pollination through the air using drone technology, was developed and the operating success of the machine was tested in walnut gardens. In the experiment gardens, female flowers on 18 branches of 5 trees each in the artificially pollinated area with a drone and in the control area were marked with colored strings. Control trees were selected from a distance that would not be possible to transport pollen with a drone. As a result of the study carried out in 2020 and 2021, the average fruit setting rate in trees pollinated by drone was determined as 94.61%. In control trees, 32.33% fruit setting was achieved. Thus, it was determined that the productivity increase in artificial pollination with AirPoll was 62.28%. In addition, in the study, Computational Fluid Dynamics (CFD) simulation analysis was performed using ANSYS Fluent 2024 R1 software to predict the downward air flow and pollen distribution in the walnut tree crown. The analysis was carried out in 680 iterations using drone propellers at a rotation speed of 4500 rpm, 4 m/s airflow and a k-w viscous model. In the analysis, it was observed that the pollen was distributed homogeneously with the determined height and the created artificial pollination environment. Based on the results obtained from the simulations, a convergence criterion of 5e-3 for continuity and 1e-6 for speed, k, w was determined. Considering all the results, the ease of use of the developed AirPoll artificial pollination machine and the successful results obtained in field trials reveal the effectiveness of the AirPoll artificial pollination machine.

This paper proposes a novel controller design using adaptation based modified super twisting control to facilitate trajectory tracking and hovering maneuvers for the quadrotor. The controller gains of the existing modified super twisting control require bounds on the disturbance for trajectory tracking and hovering of the quadrotor. In this paper, the controller gains are adapted using the proposed dynamic adaptation law without knowing the actual disturbance or their upper bounds. The controller is designed within a nonlinear framework without performing linearization of quadrotor dynamics, which enables the proposed controller to remain effective even when the states deviate significantly from their nominal values. The performance of trajectory tracking and hovering of the quadrotor in the presence of disturbance is demonstrated using numerical simulations. In order to assess the effectiveness of the controller, the performance of the adaptation based modified super twisting control is compared to the existing modified super twisting control, and the proposed controller outperforms the existing one.

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.

Unmanned Aerial Vehicles (UAVs) have emerged as efficient tools in disaster-stricken areas, facilitating efficient data dissemination for post-disaster rescue operations. However, the limited onboard energy of UAVs imposes significant constraints on their operational lifespan, thereby presenting substantial challenges for efficient data dissemination. Therefore, this work investigates a data dissemination scheme to enhance the UAVs\' bandwidth efficiency in multi-UAV-enabled Internet of Vehicles, thereby reducing UAVs\' energy consumption and improving overall system performance when UAVs hover along designated flight trajectories for data dissemination. Specifically, first, we present a software-defined network-based framework for data dissemination in multi-UAV-enabled IoV. According to this framework, we formulate a problem called C2BS (Coding-based Cooperative Broadcast Scheduling) that focuses on optimizing the UAVs\' bandwidth efficiency by leveraging the combined benefits of coding and caching. Furthermore, we demonstrate the NP-hardness of the C2BS problem by employing a polynomial time reduction technique on the simultaneous matrix completion problem. Then, inspired by the benefits offered by genetic algorithms, we propose a novel approach called the Genetic algorithm-based Cooperative Scheduling (GCS) algorithm to address the C2BS problem. This approach encompasses a coding scheme for representing individuals, a fitness function for assessing individuals, operators (i.e., crossover and mutation) for generating offspring, a local search technique to enhance search performance, and a repair operator employed to rectify infeasible solutions. Additionally, we present an analysis of the time complexity for the GCS algorithm. Finally, we present a simulation model to evaluate the performance. Experimental findings provide evidence of the excellence of the proposed scheme.

This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection.

BACKGROUND: The unmanned aerial spraying systems (UASS) have gained widespread use for plant protection in recent years. However, spray drift from UASS is a major concern when controlling weeds over large areas and warrants a thorough investigation. This study examined the atomization characteristics of the herbicide florpyrauxifen-benzyl under downwash airflow using a UASS spray test platform. Potential spray drift was assessed using a test bench (TB) and airborne drift collectors (ADCs) in the field under low wind speeds (<1 m s-1).
RESULTS: Atomization characteristics were significantly affected by the spray liquid, adjuvant, nozzle type and spray pressure. The addition of an adjuvant reduced the liquid sheet length, improved physicochemical properties and increased droplet size under the downwash airflow field. Drift evaluation in the field using the TB revealed that sediment spray drift predominantly occurred from the middle to the entire length of the device when fine-to-medium droplets were produced after the sprayer passed. ADC assessment found that higher flight altitudes and finer droplets resulted in higher drift values, whereas the addition of an adjuvant and the use of an air-induction nozzle reduced drift <3 m aboveground.
CONCLUSIONS: The combination of using TB in the target area and ADCs in the off-target area as an alternative method to determine residual droplets in the current airflow provided valuable insights into airborne drift assessment for UASS. © 2024 Society of Chemical Industry.

Over the years, thrips have transitioned from a minor nuisance to a major problem, significantly impacting the yield and quality of cotton. Unmanned aerial vehicles (UAVs) for plant protection have emerged as an effective alternative to traditional pesticide spraying equipment. UAVs offer advantages such as avoiding crop damage and enhancing pesticide deposition on the plants and have become the primary choice for pesticide application in cotton fields. In this study, a 2-year field experiment found that the thrips population in a cotton field in Xinjiang, China, exhibited gradual growth during the early flowering phase, peaking in late July. The thrips population gradually shifted from the lower canopy to the upper canopy as the cotton flowers opened layer by layer. From 09:00 to 11:00 (GMT+8) and 19:00 to 21:00 (GMT+8), thrips mainly flew outside the flowers, while from 17:00 to 19:00 (GMT+8), they mostly inhabited the inner whorls of flowers. The insecticides 10% cyantraniliprole oil dispersion and 10% spinetoram suspension concentrate, sprayed by UAV, had the best control effect on thrips, with 80.51% and 79.22% control effect after 7 days of spraying, respectively. The optimal spraying time for 10% cyantraniliprole oil dispersion was 19:00 (GMT+8), and the control effect on thrips reached 91.16% at 7 days of spraying. During the cotton flowering period, thrips inhabited flowers in the evening and flew outside during the day. The best control effect on thrips was achieved with UAV-sprayed 10% cyantraniliprole oil dispersion at 19:00 (GMT+8).

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 stimuli. 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.

Monitoring of volatile organic compounds (VOCs) in air is crucial for understanding their atmospheric impacts and advancing their emission reduction plans. This study presents an innovative integrated methodology suitable for achieving semireal-time high spatiotemporal resolution three-dimensional measurements of VOCs from ground to hundreds of meters above ground. The methodology integrates an active AirCore sampler, custom-designed for deployment from unmanned aerial vehicles (UAV), a proton-transfer-reaction mass spectrometry (PTR-MS) for sample analysis, and a data deconvolution algorithm for improved time resolution for measurements of multiple VOCs in air. The application of the deconvolution technique significantly improves the signal strength of data from PTR-MS analysis of AirCore samples and enhances their temporal resolution by 4 to 8 times to 4-11 s. A case study demonstrates that the methodology can achieve sample collection and analysis of VOCs within 45 min, resulting in >120-360 spatially resolved data points for each VOC measured and achieving a horizontal resolution of 20-55 m at a UAV flight speed of 5 m/s and a vertical resolution of 5 m. This methodology presents new possibilities for acquiring 3-dimensional spatial distributions of VOC concentrations, effectively tackling the longstanding challenge of characterizing three-dimensional VOC distributions in the lowest portion of the atmospheric boundary layer.