Simulation and verification of coupling characteristics of downwash airflow field and droplet field of agricultural unmanned aerial vehicle

Agricultural unmanned aerial vehicles (UAVs), increasingly integral to crop protection through spraying operations, are significantly influenced by their downwash fields, which in turn affect the distribution of spray droplets. The key parameters impacting spray deposition patterns are the velocity of the downwash airflow and its spatial extent. Understanding the interplay of these parameters can enhance the efficacy of UAV applications in agriculture. Previous research has predominantly focused on downwash airflow velocity, often neglecting the spatial scope of the downwash. This paper presents an applied foundational study grounded in the compressible Reynolds-averaged Navier–Stokes (RANS) equations. Leveraging a dependable k-ε turbulence model and dynamic mesh technology, it develops an effective three-dimensional computational fluid dynamics (CFD) approach to analyze the downwash field’s distribution characteristics during UAV hover. To validate the CFD method, a visualization test was conducted using EPS (expanded polystyrene foam) balls dispersed in the airspace beneath the UAV, illustrating the airflow’s spatial distribution. Additionally, a parameter η was introduced to quantify changes in the wind field’s range, enabling the mapping of the cross-sectional area of the downwash airflow at various velocities within the UAV’s airspace. The study reveals that the downwash field’s overall shape evolves from a “four-point type” to a “square-like” and then to an “ellipse-like” configuration. Lower downwash airflow velocities exhibit a more rapid expansion of the wind field area. High-velocity downwash areas are concentrated beneath each rotor, while lower-velocity zones coalesce under each rotor and extend downward, forming a continuous expanse. Within the UAV’s downwash area, the deposition of droplets is more pronounced. At a given nozzle position, an increase in downwash airflow velocity correlates with greater droplet deposition within the downwash field. This research bridges a gap in downwash field studies, offering a solid theoretical foundation for the development of future UAV downwash field models.

The use of unmanned aerial vehicles (UAVs) in agriculture is an innovation for Russia and has great potential, especially when implementing the tasks of precision farming. The directions of using UAV in agriculture are considered. It is shown that for the differentiated application of fertilizers and pesticides, it is most expedient to use UAVs of helicopter and rotary-wing type flying at low speeds (30-40 km/h) and low altitudes (0.5-1.5 m) with a large payload (300-400 kg), as they most fully meet ecological and environmental requirements and ensure the safety of modern human-machine systems. The basic requirements to the quality of the technological operation for the variable rate application of fertilizers and pesticides with the help of unmanned aerial vehicles have been developed: the rate of application of fertilizer working fluids 50-200 l/ha with a discreteness of doses of 10-15 kg active fraction per hektar (25-40 l/ha), pesticides -10-20 l/ha with discreteness of 5 l/ha, median mass diameter of droplets - 250-300 mkm, uneven distribution of working fluid over the operating width is no more than 15 percent, demolition of working fluids outside the treated area is no more than 20 percent, time of control index setting at the boundaries of the allocated plots is no more than 1.1 s; operating speed of the flight is not more than 40 km/h, the altitude of the flight when applying fertilizers and pesticides equals 1.0-1.5 m.

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

It is generally accepted that UAVs - unmanned aerial vehicles, otherwise known as drones, are used only for military purposes. This is a misconception: since the 60s of the last century, Russian and American specialists have been building unmanned UAVs not only for the armies of their countries, but also for peaceful purposes. The purpose of the article is to study the possibilities and progress in the development of drones for civil and needs. In preparing and writing the article, such research methods as general scientific methods of historical and logical, abstract and concrete, analysis and synthesis, comparisons and analogies were used. The main result of the study is the conclusion that unmanned aerial vehicles can be successfully used for civilian purposes, and not just for military purposes. Drones are now actively used for agricultural and environmental purposes. They are called “eco-drones”. They are no different from ordinary ones; the prefix is designed to emphasize their purely peaceful, scientific purpose.

Accurate measurement of the downwash flow field of plant protection unmanned aerial vehicles (UAVs) is essential for analyzing the spatial distribution of droplets. To realize on-line rapid detection of the downwash flow field of a multi-rotor UAV, a flexible polypropylene detection device based on the principle of full bridge strain effect was proposed. Its performance principle was based on the physical deformation caused by wind pressure. The Fluid Flow and Static Structural modules of ANSYS 16.0 finite element software were used to simulate one-way fluid-solid coupling interaction. The surface of the resistive strain gauge embedded in the flexible detecting structure responded well to wind speed variation, hence it was suitable for downwash airflow wind field detection. By solving the strain force on the surface of the flexible detection structure, the length and layout of the grating wire of the strain gauge on the surface of the flexible detection structure were optimized. Meanwhile at 4 m·s−1 wind speed, the output voltage at varied bridge flexible acquisition systems in the acquisition card was measured. Results indicated coefficient of variation of 3.67%, 1.63% and 1.5%, respectively, which proved the good data acquisition consistency of the system. Through calibration test, the regression equation for the relationship between output voltage and wind speed for three unique sensor signal measuring circuits was established. The determination coefficients R2 for single bridge, half bridge and full bridge circuits were 0.9885, 0.9866 and 0.9959, respectively. In conclusion, by applying the multi-rotor plant protection UAV test platform, the results indicated the maximum relative error of the wind speed at each sampling point of the system at 1.0 m altitude was below 5.61%. Simulated and measured value had an RMSE maximum error of 0.1246 m·s−1. Moreover, downwash airflow detection not only has high accuracy but also has high sensitivity. Thus, there is convenience and practicability in the plant protection offered by this approach. The rapid measurement of UAV wind field and the established two-dimensional wind field model can provide a basis for precise application of agricultural aviation.

Unmanned aerial vehicles (UAVs) or drones have been developed significantly over the past two decades, for a wide variety of applications such as surveillance, geographic studies, fire monitoring, security, military applications, search and rescue, agriculture, etc. In agriculture, for example, remote sensing by means of unmanned aerial vehicles has proven to be the most efficient way to monitor crops from images. Unlike remote sensing from satellite images or taken from manned aircraft, UAVs allow capturing images of high spatial and temporal resolution, thanks to their maneuverability and capability of flying at low altitude. This article presents an extensive review of the literature on crop monitoring by UAV, identifying specific applications, types of vehicles, sensors, image processing techniques, among others. A total of 50 articles related to crop monitoring applications of UAV in agriculture were reviewed. Only journal articles indexed in the Scopus database with more than 50 citations were considered. It was found that cereals are the most common crops where remote sensing has been applied so far. In addition, the most common crop remote sensing applications are related to precision agriculture, which includes the management of weeds, pests, diseases, nutrients and others. Crop phenotyping is also a common application of remote sensing, which consists of the evaluation of a crop’s physical characteristics under environmental changes, to select the plants or seeds with favorable genotype and phenotype. Besides, multirotor is the most common type of UAV used for remote sensing and RGB and multispectral cameras are mostly used as sensors for this application. Finally, there is a great opportunity for research in remote sensing related to a wide variety of crops, crop monitoring applications, vegetation indexes and photogrammetry.

Unmanned Aerial Vehicles (UAVs) and blockchain Technologies are relevant systems that have a significant performance in numerous sectors. In particular, applying these emerging technologies will affect positively the agricultural ecosystem. In this paper, we investigate the opportunities offered by UAV s and blockchain (BC) in the agricultural sector. We review recent research efforts in the subject with a synthesis illustrated by a classification table. Finally, open challenges and future directions for IoT-based agriculture applications are discussed.

Agricultural pests and diseases affect grain yield and quality. In addition to the use of chemical agents, the biological control of diseases and insect pests has attracted more and more attention due to an urgent need for environmental protection. However, traditional operational methods do not integrate well with biological control technology. With the advantages of simple operation, low operating cost, high operating efficiency and wide application range, use of multirotor unmanned aerial vehicles (UAVs) in biological control is of great significance. An intelligent delivery system comprising an insect natural enemy (Trichogramma) capsule based on an M45 electric multirotor UAV was built to release Trichogramma ostriniae in corn fields under full autonomous mode. During operation, the M45 UAV can fly accurately in accordance with operational requirements; average deviation between the actual and planned routes was 0.24 m, the average flight speed was 9.18 m s-1 and the average altitude was 5.08 m. The effective coverage rate of natural enemies was 99.33% and the coverage redundancy was 36.75%. After delivery, an investigation into the field control effect showed an average control effect of 83.70%, which can result in effective pest control. A natural enemy carrier intelligent delivery system based on an M45 electric multirotor UAV was found to have good flight performance. In addition, natural enemies were evenly distributed, giving even coverage of the target fields. The system had a good control effect. © 2021 Society of Chemical Industry.

The accelerating impacts of climate change continue to challenge global agriculture, increasing the need for adaptive and sustainable technological solutions. In this context, Unmanned Aerial Vehicles (UAVs) have evolved from experimental tools to key components of precision agriculture, offering advanced capabilities for monitoring, diagnosis, and targeted intervention. This Research Topic explores how UAVs, combined with remote sensing, AI, and operational strategies, can advance climate-smart agriculture. The topic focuses on innovative technologies and UAV applications in precision agriculture for mitigating climate change, highlighting recent advancements.The four contributions in this collection illustrate UAVs' potential to improve resource-use efficiency, enhance plant protection, enable early detection of biotic stress, and support scalable, data-driven decision-making. Together, these studies underline both the versatility of UAV technologies and the need for continued innovation to address environmental variability, operational challenges, and integration with complementary sensing platforms.Regarding UAV for spraying, the: operational variability and deposition efficiency,

Increasing the level of efficiency of land use management of pasture lands is one of the most important modern problems in the context of the agro-industrial policy of Kazakhstan. Using the method of content analysis of scientific literature, the authors analyze the utilization state of the unmanned aerial vehicles in agriculture, as well as the possibilities of their use for monitoring pasture lands. The authors assess the possible economic benefit of using this technology for land management. To assess the feasibility of using unmanned aerial vehicles for monitoring pasture lands, a SWOT analysis is conducted. The results of the study allow concluding that using unmanned aerial vehicles for monitoring pasture lands leads to an increase in the level of land use management by providing a wide range of opportunities and achieving an economic benefit for all agricultural production entities.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

In the golden era of digitalization, flow calculation research based on a numerical approach has become increasingly popular in the scientific world and has played a vital role in revealing the aerodynamic characteristics of various aircraft models. This paper, belonging to computational fluid dynamics, describes the process and results of a numerical simulation approach using the software OpenFOAM for an unmanned aerial vehicle (UAV) in the case of forwarding flight with a low Reynolds number. With regard to the numerical approach, the aerodynamic properties are investigated through the turbulence model k-ω-SST (shear stress transport), which is a part of the turbulence calculation approach Reynolds average Navier–Stokes (RANS). Furthermore, the entire process of mesh generation and the numerical calculation process by simpleFoam in OpenFOAM, which is the solver using the semi-implicit method for pressure-linked equations (SIMPLE) algorithm for steady, incompressible flow, are also depicted in this paper. In addition, the characteristics of the flow field surrounding the model and the characteristic aerodynamic coefficients (drag coefficient, lift coefficient, lift-to-drag ratio versus angle of attack and pressure coefficients, and skin-friction coefficients for special positions on the wing) of the model are also clearly illustrated. Finally, the simulation illustrations in ParaView, including the velocity field, pressure field and vortices in the stagnation region, are shown. The process description and results contribute a clear picture of the numerical execution steps in OpenFOAM and aerodynamic knowledge for a particular unmanned aerial vehicle model to the computational fluid dynamics field in the aerospace industry.

The study of unmanned aerial vehicles in agriculture is limited by regulations. Each country has its own restrictions in the form of flight altitude, maximum takeoff weight, and the required package of documents. The article discusses the main regulatory restrictions for the use of drones in various countries of the world.

CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability.