Unmanned Aerial Vehicle Applications Research Articles

Application Research of Unmanned Aerial Vehicle Remote Sensing Technology in Agricultural Pest and Disease Monitoring

This study aims to explore the application of unmanned aerial vehicle (UAV) remote sensing technology in agricultural pest and disease monitoring to improve monitoring efficiency and accuracy. Through literature review and empirical research, the current application status, advantages, and challenges of UAV remote sensing technology in agricultural pest and disease monitoring were systematically studied. The study first analyzed and summarized the principles and characteristics of UAV remote sensing technology, and then conducted case studies and field investigations on its specific application in agricultural pest and disease monitoring. The research results show that UAV remote sensing technology has the advantages of high resolution, high spatiotemporal coverage, and high efficiency in agricultural pest and disease monitoring. It can provide multi-source data acquisition and analysis during crop growth to achieve rapid identification and dynamic monitoring of agricultural pest and disease. The findings of this study have important implications for guiding the application and promotion of UAV remote sensing technology in agricultural pest and disease monitoring.

Application of unmanned aerial vehicle (UAV) for a safer roof inspection at high rise building

Application of unmanned aerial vehicle (UAV) for a safer roof inspection at high rise building

5G-enabled UAVs for energy-efficient opportunistic networking

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy.An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations.Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

A multiple directions turning vehicle counting method at intersections based on arbitrary-oriented detection and stack Long Short-Term Memory

The traffic flow parameters of turning vehicles at urban road intersections play an important role in the intelligent traffic system. Traffic road monitoring is difficult to cover large intersection scenes. With the application of unmanned aerial vehicles in traffic, drone-based intersection monitoring has great potential due to the large perspective. But there is rarely research on multiple turning vehicle counting at intersections based on drones. A few existing multiple turning vehicle counting methods are based on vehicle detection and tracking methods. However, the vehicles at the intersection have various orientations, the accurate information of the vehicle cannot be obtained by detection, and the tracking process is complicated and redundant for the counting task. Based on the traffic video at intersections taken by unmanned aerial vehicles, we propose a eight turnings vehicle counting model without complex tracking. Firstly, a spatial attention and channel adaptive attention model net is proposed for arbitrary-oriented vehicle detection to get the orientation and position of vehicles. Secondly, a turning spatio-temporal counting feature for different turning vehicles and its extraction method are proposed. Finally, the stack Long Short-Term Memory net based counting model is designed to process the turning spatio-temporal counting feature and counting eight different turnings vehicles at intersection. The experiments show that the proposed method can realize counting eight different turnings vehicles simultaneously in drone-based traffic video at the intersection without relying on complex multi-target tracking. The average counting accuracy has reached 98.18%.

Compact-size Lightweight Beam-reconfigurable ESPAR Antenna with Parasitic Elements for UAV Applications

This paper presents a compact lightweight beam-reconfigurable antenna system for unmanned aerial vehicles (UAVs). The antenna system consists of a central active monopole element surrounded by eight parasitic elements, which can be controlled using PIN diodes to switch the beam across four elevation angles and eight azimuth beams. This beam-reconfigurable antenna system has several advantages over traditional UAV antennas, including light weight, efficiency, and compactness. The antenna system operates at 5.09 GHz and achieves a measured peak gain of 4.55 dBi, with a remarkably low weight of 9 g and a size of 1.00λ0×1.00λ0×0.22λ0.

Systematic Research and Application of a 5G Medical Unmanned Aerial Vehicle to Deliver COVID-19 Nucleic Acid Samples.

The purpose of this study was to investigate the feasibility and effectiveness of COVID-19 throat swab samples delivered by medical drones in epidemic prevention and control. This study was carried out in both southern and northern hospital districts of the Affiliated Hospital of Jiangnan University from May to October 2022. The main participants were the Affiliated Hospital of Jiangnan University and Zhejiang Antwork Technology Co., Ltd. We first constructed an urban medical unmanned aerial vehicle (UAV) delivery system and developed a UAV-specific storage box for COVID-19 samples. The UAV system was used to transport COVID-19 throat swab samples from the northern hospital district to the southern hospital district, and the following indexes were obtained: (1) flight time of COVID-19 samples delivered by UAV, (2) real-time temperature of COVID-19 nucleic acid samples during transportation, and (3) the time of distribution of COVID-19 nucleic acid samples by road traffic as measured using the Baidu Maps application, compared with the flight time of UAV. The COVID-19 sample delivery system for urban medical UAV mainly consists of intelligent logistics UAV, low-temperature COVID-19 throat swab sample storage box, unmanned logistics hub, and cloud operation control platform. The flight distance between the northern and southern districts of the Affiliated Hospital of Jiangnan University was 10 km, and the ground distance was 24 km. From May 11 to October 28, 2022, a total of 1,190 UAV flights occurred. The average flight time was 13 minutes, which was 40 to 70 minutes faster than the average road travel time required for manual delivery of COVID-19 throat swab samples. At different time points in the day, UAV delivery efficiency increased by 67.5% to 82%. The use of 5G with the Internet of Things and UAV technology to deliver nucleic acid samples has the characteristics of fast speed, being unaffected by ground traffic conditions, and the ability to ensure the safety of nucleic acid samples in the transportation process, which is worthy of further study.

Enabling Information Transmission in Low-Throughput Wireless Channels for Aerial Disaster Monitoring Systems

The utilization of Unmanned Aerial Vehicles (UAVs) for disaster relief has significantly increased in recent years, especially in the fields of aerial monitoring and communication support. At the same time, research on wireless communication channels between UAVs operating in disaster areas and their base station has been neglected, despite its importance for system operability and information transmission. Therefore, this paper addresses requirements of a suitable data channel on the use case of an Aerial Monitoring System for distributed disaster networks. We put special emphasis on possible applications using low-throughput technologies, like LoRa, to enable information transmission on low-cost hardware without centralized infrastructure, which is highly important for UAV applications in disaster scenarios. Specifically, we propose the aggregation of topology information to reduce message sizes and increase usability under challenging network conditions. Our results highlight that the aggregated monitoring information can be transferred to the base station with reasonable delay and reliability, possible with a low-throughput channel as low as 300 bit/s. At the same time, the aggregated information still maintains an adequate representation of the monitored topology of the distributed disaster network.

Application of Unmanned Aerial Vehicle (UAV) Sensing for Water Status Estimation in Vineyards under Different Pruning Strategies.

Pruning determines the plant water status due to its effects on the leaf area and thus the irrigation management. The primary aim of this study was to assess the use of high-resolution multispectral imagery to estimate the plant water status through different bands and vegetation indexes (VIs) and to evaluate which is most suitable under different pruning management strategies. This work was carried out in 2021 and 2022 in a commercial Merlot vineyard in an arid area of central Spain. Two different pruning strategies were carried out: mechanical pruning and no pruning. The stem water potential was measured with a pressure chamber (Ψstem) at two different solar times (9 h and 12 h). Multispectral information from unmanned aerial vehicles (UAVs) was obtained at the same time as the field Ψstem measurements and different vegetation indexes (VIs) were calculated. Pruning management significantly determined the Ψstem, bunch and berry weight, number of bunches, and plant yield. Linear regression between the Ψstem and NDVI presented the tightest correlation at 12 h solar time (R2 = 0.58). The red and red-edge bands were included in a generalised multivariable linear regression and achieved higher accuracy (R2 = 0.74) in predicting the Ψstem. Using high-resolution multispectral imagery has proven useful in predicting the vine water status independently of the pruning management strategy.

Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception.

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual-inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Image fusion algorithm for the collaborative use of unmanned aerial vehicles

Currently, the applicable scope of unmanned aircraft application is increasingly expanding. The promising field of unmanned aircraft enhancement is the implementation of some collaborative actions during controlled flight. This paper considers some issues of the group application of unmanned aerial vehicles (UAV) related to the coordinated planning and control of UAVs performing surveillance missions. Performing aerial search operations is technically complicated by the requirement to recognize a search object in arbitrary conditions, which can be both simple and severe environment. The search area is limited by the UAV capabilities, so, in order to improve the efficiency of search operations, UAVs are combined into groups. An algorithm for solving the problem of object search in arbitrary conditions by a group of unmanned aircraft is proposed. The advantage of search by a group of unmanned aircraft is the coverage of the larger search area in a conventional unit of time. This paper addresses the unmanned aircraft configuration, containing both the means of collaborative flight operation and a synthetic vision system. The image obtained by the synthetic vision system is both a source of navigation information and a means which reliably determines the result of search operations. Depending on the conditions of search operations, the image obtained by the synthetic vision system may require additional processing to use as intended. A fusion algorithm is proposed, which is characterized by adaptive adjustment of parameters in each frame individually for different image fragments. Based on the results obtained, it is planned to create a new product for commercial operation of unmanned aircraft.

Tank-mix adjuvants improved spray performance and biological efficacy in rice insecticide application with unmanned aerial vehicle sprayer.

The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank-mix adjuvants, Wonderful Rosin (Adjuvant-1) and Tiandun (Adjuvant-2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant-1 and Adjuvant-2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant-1) and 20.23% (Adjuvant-2) over applications without tank-mix adjuvant 7 days after treatment. The judicious selection of tank-mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank-mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry.

A low-profile ultra-wideband hexagonal-patch antenna for UAV-based applications

ABSTRACT A compact, low-profile, and ultra-wideband antenna is always desirable for airborne Unmanned Aerial Vehicles (UAV) applications. In this paper, an ultra-wideband monopole antenna is presented with the benefit of a compact and low-profile design structure. The antenna design enables efficient communication without interfering with the UAV’s other components. The antenna is designed using a slotted-hexagonal-patch fed from a co-planar microstrip feed. The bow-tie-shaped slot along with a few other rectangular-shaped parasitic slots are loaded in the hexagonal patch for bandwidth enhancement. The coplanar feed with top ground offers minimum interference with the UAV structure. The non-conducting bottom plane allows seamless integration without causing any interference to the antenna. An equivalent circuit model (ECM) of the proposed antenna is also developed. The antenna structure is fabricated and the measured results are validated. A good agreement between the measured and simulated results is observed. The antenna covers an ultra-wideband ranging from 3 GHz to 13.5 GHz with a maximum gain of 4.0dBi. The size of the antenna is 0.24λ0 ×0.29λ0 ×0.01λ0. The developed antenna, therefore, demonstrates that it is a strong contender for ultra-wideband UAV-based applications.

Design of Series-Fed Circularly Polarized Beam-Tilted Antenna for Microwave Power Transmission in UAV Application

In response to the increasing deployment of unmanned aerial vehicles (UAVs) across various sectors, the demand for efficient microwave power transmission (MPT) systems for UAVs has become paramount. This study introduces series-fed circularly polarized (CP) and passively beam-tilted patch array antennas designed to enhance MPT in UAV applications, with the intention of addressing the needs related to extending flight times and improving operational efficiency. The radiating element of the proposed antennas employs the conventional model of the patch with truncated corners for CP operation, with transmission line lengths optimized for beam tilt to ensure precise energy transfer. Additionally, an open stub is integrated into the broadside series-fed antenna to improve impedance matching, which is crucial for maintaining signal integrity. The proposed design achieves right-hand circular polarization (RHCP) with an axial ratio (AR) below 3 dB across the operating band, indicative of its effectiveness in diverse UAV operational contexts. Prototypes of each proposed antenna were fabricated and measured according to the beam tilting angle. The measured RHCP realized gains of the proposed antennas are 14.59, 13.09, 13.07, and 10.71 dBic at the tilted angles of 0°, 15°, 30°, and 45°, respectively, at 5.84 GHz.

Management of a post-disaster emergency scenario through unmanned aerial vehicles: Multi-Depot Multi-Trip Vehicle Routing with Total Completion Time Minimization

One of the most valuable and promising applications for Unmanned aerial vehicles (UAVs) is in natural disaster management, where these aircraft can operate autonomously without any need for human intervention during their flights.In this paper, we foster the interface of Operational Research with computer science in general and sensor networking in particular by focusing on managing a post-disaster emergency scenario where the use of a fleet of UAVs helps rescue teams identify people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Time minimization (MDMT-VRP-TCT). The main novelty of the MDMT-VRP-TCT is the combination of the following three features: multi-depot, multi-trip, and completion time minimization.We propose a mixed-integer linear programming (MILP) formulation, develop a matheuristic framework to address large instances, and present an extended set of experiments to test the performance of the proposed matheuristic: first, we compare the matheuristic with the MILP formulation on a set of small instances (up to 30 nodes); then, we compare our matheuristic with two heuristics from networking literature, showing that it outperforms the existing algorithms.

Application of unmanned aerial vehicles in emergency radiation monitoring

The paper is based on experiments within a project to prepare tutorial tasks on a training centre for testing radiation detectors fixed on unmanned aerial vehicles (UAVs) and in unmanned ground vehicles. The paper reports on a small UAV with NaI(Tl) 2“ x 2” detector for radiation monitoring in an emergency event and for radioactive source chasing and detection. The paper is focused on some special cases with the possibility to determine local air kerma rates and the activities of point sources. The detector was calibrated for air kerma rates from 5 nGy h−1 to 20 μGy h−1. The verified model of the NaI(Tl) 2″ x 2″ detector including the electronic module assembled on a UAV was designed in a Monte Carlo simulation. The model was used for calculating the air kerma rates at various altitudes above the ground both for a natural background and for selected point sources. The response functions for 137Cs, 131I, 192Ir, 60Co and 99Mo point sources were calculated by the Monte Carlo simulation to estimate their activities. Experiments were carried out with an NaI(Tl) 2“ x 2” detector fixed on a Robodrone UAV and flying over 60Co and 137Cs point sources over the airfield area and the local air kerma rates and the activities of the point sources were calculated with the AGAMA post-processing program. In addition, the response functions for 137Cs distributed uniformly on the surface were calculated by Monte Carlo simulation for future emergency purposes. The detection limits of selected man-made nuclides (point sources) were calculated.

Relay-Based Identification of Aerodynamic and Delay Sensor Dynamics With Applications for Unmanned Aerial Vehicles

Relay-Based Identification of Aerodynamic and Delay Sensor Dynamics With Applications for Unmanned Aerial Vehicles

A hybrid multi-objective evolutionary algorithm with high solving efficiency for UAV defense programming

Emerging Unmanned Aerial Vehicle (UAV) application patterns, including the Loyal Wingman and Unmanned Swarms, have significantly challenged the administration and defense against illegal or trespassing UAVs. The weapon-target assignment (WTA) problem, a famous combinatorial optimization problem in military operations research, is decisive for the success of UAV defense programming. Related investigations face two main challenges: 1) Constructing a multi-objective optimization model reflecting the complexities of new UAV application patterns, and 2) solving the time-consuming WTA problem with high-dimension decision variables as it is NP-complete. This paper introduces a modified differential evolution based on a new mapping method called Pareto-Optimal-Matching that solves multi-objective binary optimization problems and constructs a 3 objective Sensor-Weapon-Target Assignment model. The proposed algorithm overcomes the inherent limitation of traditional differential evolution, which only performs well in continuous problems. Promising results were obtained based on different simulation metrics under various problems (proposed model, MOKP, and ZDT5), validating the superior performance of the proposed algorithm.

Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

AbstractOver the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications

AbstractThis paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.

Enhancing Visualization of Roman Aqueducts and Water Canalization in Volubilis, Morocco: A Study Using UAV Photogrammetry and Field Observations

This study investigates the application of Small Unmanned Aerial Vehicle (UAVs) photogrammetry alongside field observations to enhance the visualization of aqueduct and water canalization systems in Volubilis, Morocco, a UNESCO World Heritage site. Focused on the Roman period, the research explores the aqueducts’ role in supplying water to diverse structures, including private houses, cisterns, public fountains, baths, official buildings, and public latrines. The city’s growth from the Mauretanian period to its establishment as a Roman municipality in 42 A.D. witnessed the development of significant structures and an intricate water management system. The aqueducts sourced water from the springs of Fertassa and Laksar, utilizing the topography and gravity to direct water from upland to Volubilis. In contrast to previous studies relying on surface observations and/or geophysical methods, the research herein study integrates both photogrammetry and field observation techniques. Orthomosaics maps, digital 3D models, and Digital Elevation Models (DEMs) obtained from Small Unmanned Aerial Vehicles (UAVs) surveys of Volubilis were used to illustrate the aqueducts’ orientation and direction of canalization within the city. The complex network of water systems within houses is exemplified by the House of Venus and the House of Orpheus. The study identifies three main categories: surface and subsurface canals, intra-wall conduits made of ceramic or lead, and/or a combination of both. Despite gaps resulting from the loss or collapse of entire structures, the study successfully connects water conduits from aqueducts to various locations in Volubilis. The DEM underscores the crucial role of gravity in water distribution, prompting inquiries into the mechanisms involved and suggesting potential employment of surface, siphoning, over-wall canalization, and/or a combination thereof. Photogrammetric analysis, coupled with meticulous field observations, contributes to the reconstruction of a comprehensive conceptual model of the hydraulic system of Volubilis. The latter showcases elaborate water basins, latrines, baths, and fountains, with water directed through a vertical ceramic pipe fed by underground channels originating from the main aqueduct crossing the center of Volubilis. This research provides valuable insights into Roman water supply and infrastructure management, highlighting the seamless integration of photogrammetry, field observations, and historical evidence. The findings significantly contribute to our understanding of ancient aqueduct systems and their role in sustaining urban life in the Roman city of Volubilis.