Search Results (28)

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article

Unmanned Aerial Vehicles (UAVs) can deliver rapid, spatially resolved measurements of volcanic gases that often precede eruptions, yet most deployments remain manual or preplanned and are slow to react to seismic unrest. In the present work, we present a simulation-validated design of an earthquake-triggered, autonomous workflow for early detection of CO₂ anomalies, demonstrated through a conceptual case study focused on the Santorini caldera. The system ingests real-time seismic alerts, generates missions automatically, and executes a two-stage sensing strategy: a fast scan to build a coarse CO₂ heatmap followed by targeted high-precision sampling at emerging hotspots. Mission planning includes wind-and terrain-aware flight profiles, geofenced safety envelopes and a facility-location approach to landing-site placement; in a Santorini case study, we provide a ring of candidate launch/landing zones with wind-contingent usage, illustrate adaptive replanning driven by heatmap uncertainty and outline calibration and quality-control steps for robust CO₂ mapping. The proposed methodology offers an operational blueprint that links seismic triggers to actionable, georeferenced gas information and can be transferred to other island or caldera volcanoes. Full article

Above-ground biomass in agroforestry refers to the total mass of living vegetation, primarily trees and shrubs, integrated into agricultural landscapes. It plays a key role in climate change mitigation by capturing and storing carbon. Accurate estimation of above-ground biomass in agroforestry systems requires effective drone deployment and sensor management. This study presents a detailed methodology for biomass estimation using Unmanned Aircraft Systems, based on an experimental campaign conducted in the Campania region of Italy. Multispectral drone platforms were used to generate calibrated reflectance maps and derive vegetation indices for biomass estimation in agroforestry landscapes. Integrating field-measured tree attributes with remote sensing indices improved the accuracy and efficiency of biomass prediction. Following the assessment of mission parameters, flights were conducted using a commercial drone to demonstrate consistency of results across multiple altitudes. Terrain-follow mode and high image overlap were employed to evaluate ground sampling distance sensitivity, radiometric performance, and overall data quality. The outcome is a defined process that enables agronomists to effectively estimate above-ground biomass in agroforestry landscapes using drone platforms, following the procedure outlined in this paper. Predictive performance was evaluated using standard model metrics, including R², RMSE, and MAE, which are essential for replicability and comparison in future studies. Full article

Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding and gusts. These wind speed oscillations generate unsteady forces that can destabilise UAV flight, particularly for small vehicles. Additionally, predicting their formation requires high-resolution Computational Fluid Dynamics (CFD) models, as current weather forecasting tools lack the resolution to capture these phenomena. However, such models require 3D representations of study areas with high geometric consistency and detail, which are not available for most cities. To address this issue, this work introduces an automated methodology for urban CFD mesh generation using open-source data. The proposed method generates error-free meshes compatible with OpenFOAM and includes tools for geometry modification, enhancing solver convergence and enabling adjustments to mesh complexity based on computational resources. Using this approach, CFD simulations are conducted for the city of Ourense, followed by an analysis of their impact on UAV operations and the integration of the system into a trajectory optimisation framework. The CFD model is also validated using experimental anemometer measurements. Full article

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where direct sensor access is impractical due to terrain constraints or operational limitations. This research addresses these challenges through a novel hybrid optimization framework that combines integer linear programming (ILP) with multiple traveling salesperson problem (mTSP) algorithms for drone-based data collection in wireless sensor networks (WSNs). The methodology employs a two-phase approach, where ILP optimally determines strategic access point locations for sensor clustering based on communication capabilities, followed by mTSP optimization to generate efficient inter-AP flight trajectories rather than individual sensor visits. Comprehensive simulations across diverse network configurations and drone quantities demonstrate consistent performance improvements, with travel distance reductions reaching 32% compared to conventional mTSP implementations. Comparative evaluation against established clustering algorithms including Voronoi, DBSCAN, Constrained K-Means, Graph-Based clustering, and Greedy Circle Packing confirms that ILP consistently achieves optimal access point allocation while maintaining superior routing efficiency. Additionally, a novel quality assessment metric quantifies sensor grouping effectiveness, revealing that ILP-based clustering advantages become increasingly pronounced with higher sensor densities, providing substantial operational benefits for large-scale wireless sensor network deployments. Full article

In mountainous citrus orchards, the application of conventional ground sprayers for the control of citrus red mite (Panonychus citri) is often constrained by complex terrain and low operational efficiency. The Unmanned Aerial Spraying System (UASS), due to its low-altitude, low-volume, and high-maneuverability characteristics, has emerged as a promising alternative for pest management in such challenging environments. To evaluate the spray performance and field efficacy of different UASS types in controlling P. citri, five representative UASS models (JX25, DP, T1000, E-A2021, and T20), four mainstream pesticide formulations, and four novel tank-mix adjuvants were systematically assessed in a field experiment conducted in a typical hilly citrus orchard. The results showed that T20 delivered the best overall spray deposition, with upper canopy coverage reaching 10.63%, a deposition of 3.01 μg/cm², and the highest pesticide utilization (43.2%). E-A2021, equipped with a centrifugal nozzle, produced the finest droplets and highest droplet density (120.3–151.4 deposits/cm²), but its deposition and coverage were lowest due to drift. Nonetheless, it exhibited superior penetration (dIPR 72.3%, dDPR 73.5%), facilitating internal canopy coverage. T1000, operating at higher flight parameters, had the weakest deposition. Formulation type had a limited impact, with microemulsions (MEs) outperforming emulsifiable concentrates (ECs) and suspension concentrates (SCs). All adjuvants improved spray metrics, especially Yimanchu and Silwet, which enhanced pesticide utilization to 46.8% and 46.4% for E-A2021 and DP, respectively. Adjuvant use increased utilization by 4.6–11.9%, but also raised ground losses by 1.5–4.2%, except for Yimanchu, which reduced ground loss by 2.3%. In terms of control effect, the rapid efficacy (1–7 days after application, DAA) of UASS spraying was slightly lower than that of ground sprayers—electric spray gun (ESG), while its residual efficacy (14–25 DAA) was slightly higher. The addition of adjuvants improved both rapid and residual efficacy, making it comparable to or even better than ESG. E-A2021 with 5% abamectin·etoxazole ME (5A·E) and Yimanchu achieved 97.4% efficacy at 25 DAA. Among UASSs, T20 showed the rapid control, while E-A2021 outperformed JX25 and T1000 due to finer droplets effectively targeting P. citri. In residual control (14–25 DAA), JX25 with 45% bifenazate·etoxazole SC (45B·E) was most effective, followed by T20. 5A·E and 45B·E showed better residual efficacy than abamectin-based formulations, which declined more rapidly. Adjuvants significantly extended control duration, with Yimanchu performing best. This study demonstrates that with optimized spraying parameters, nozzle types, and adjuvants, UASSs can match or surpass ground spraying in P. citri control in hilly citrus orchards, providing valuable guidance for precision pesticide application in complex terrain. Full article

With the increasing mining depth, the stability of open-pit mine slopes has become an increasingly important concern. This study focuses on an open-pit mine in Southwest China and utilizes unmanned aerial vehicle (UAV) technology to gather data from these high and steep slopes. First, high-precision digital surface models and digital orthophoto data are collected using UAV terrain-following flight technology. However, two major challenges arise when applying geographic information systems (GISs) to this issue. The first challenge is that the extreme steepness of the slopes causes overlapping lithological layers at the same location, which GISs cannot resolve. The second challenge is that GISs cannot assess the influence of faults on landslides by calculating three-dimensional spatial distances. To overcome these issues, this study proposes the construction of a detailed 3D geological model for the entire mining area. This model allows for a more precise analysis of the lithology and fault spatial distances. A GIS is then applied to analyze the slope, curvature, and slope direction. Multi-source data fusion is employed to link spatial coordinates and create a dataset for further analysis. Five machine learning models for landslide prediction are compared using this dataset. Based on these comparisons, a high-precision random forest and slope boosting coupled method is developed to enhance the landslide prediction accuracy. Finally, a numerical simulation of a regional focus area is conducted, simulating the excavation process of an open-pit mine and analyzing the timing, location, and state of potential landslides. The results indicate that combining machine learning and multi-source data fusion provides a highly accurate, efficient, and straightforward method for landslide prediction in the high and steep slopes of open-pit mines. Full article

Valley path planning on 3D terrains holds significant importance in navigating and understanding complex landscapes. This specialized form of path planning focuses on finding optimal routes that adhere to the natural contours of valleys within three-dimensional terrains. The significance of valley path planning lies in its ability to address specific challenges presented by valleys, such as varying depths, steep slopes, and potential obstacles. By following the natural flow of valleys, path planning can enhance the efficiency of navigation and minimize the risk of encountering difficult terrain or hazards. In recent years, an increasing number of researchers have focused on the study of valley path planning on 3D terrains. This study presents a valley path planning method utilizing the NSGA-II (Non-dominated Sorting Genetic Algorithm II) approach. To ensure that the paths generated by the algorithm closely follow the valley lines, the algorithm establishes an optimization function that includes three optimization criteria: mean altitude, flight route length, and mean offset. To test the performance of this algorithm, we conducted experiments based on workspaces based on three datasets full of 3D terrains and compared it with three baseline algorithms. The evaluation indicates that the suggested algorithm successfully designs routes that closely follow the valley contours. Full article

The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties. Full article

The rapid growth of urban populations and the need for sustainable urban planning and development has made Unmanned Aerial Vehicles (UAVs) a valuable tool for data collection, mapping, and monitoring. This article reviews the applications of UAV technology in sustainable urban development, particularly in Malaysia. It explores the potential of UAVs to transform infrastructure projects and enhance urban systems, underscoring the importance of advanced applications in Southeast Asia and developing nations worldwide. Following the PRISMA 2020 statement, this article adopts a systematic review process and identifies 98 relevant studies out of 591 records, specifically examining the use of UAVs in urban planning. The emergence of the UAV-as-a-service sector has led to specialized companies offering UAV operations for site inspections, 3D modeling of structures and terrain, boundary assessment, area estimation, master plan formulation, green space analysis, environmental monitoring, and archaeological monument mapping. UAVs have proven to be versatile tools with applications across multiple fields, including precision agriculture, forestry, construction, surveying, disaster response, security, and education. They offer advantages such as high-resolution imagery, accessibility, and operational safety. Varying policies and regulations concerning UAV usage across countries present challenges for commercial and research UAVs. In Malaysia, UAVs have become essential in addressing challenges associated with urbanization, including traffic congestion, urban sprawl, pollution, and inadequate social facilities. However, several obstacles need to be overcome before UAVs can be effectively deployed, including regulatory barriers, limited flight time and range, restricted awareness, lack of skilled personnel, and concerns regarding security and privacy. Successful implementation requires coordination among public bodies, industry stakeholders, and the public. Future research in Malaysia should prioritize 3D modeling and building identification, using the results of this study to propel advancements in other ASEAN countries. Full article

The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucial role in the mapping process. Researchers are exploring solutions that use image-based techniques such as structure from motion (SfM) to produce topographic maps using UAVs while accessing locations with extremely high accuracy and minimal surface measurements. Advancements in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and UAV global navigation satellite system observation files are utilized to calculate the camera positions with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The evaluation of ground checkpoints using the proposed PPK methodology with one ground control point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical, nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following) at an altitude of 120 m. These results suggest that the proposed methodology can achieve high positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps. Full article

With precision agriculture developing rapidly worldwide, water-saving, energy-saving, environment-friendly, and efficient agricultural production activities are effective ways to address human needs for agricultural products under the conditions of intensifying climate change, limited available arable land resources, and rapid population growth. Ground-based plant-protection machinery applied to large fields has difficulty solving the pest and disease prevention needs of mountain orchards since they feature undulating topography changes and low standardization of orchards. Unmanned aerial vehicles (UAVs) have broad development prospects in pest control in mountain orchards because of their advantages of not being restricted by terrain, strong maneuverability, and hover ability. This paper reviews the recent development of plant-protection UAVs from three perspectives, i.e., positioning and navigation technology, flight attitude control technology, and route planning in mountain orchards. We highlight that the future research should focus on following technology development, including (1) positioning navigation technology with high positioning accuracy and strong anti-interference capability, (2) intelligent control technology with high dynamic stability and better calculation accuracy, and (3) the optimization of the route-planning algorithm covering multiple constraints and the cluster cooperative operation scheme of plant-protection UAVs applicable to mountain orchards. These reviewed results could provide a reference for the future development of plant-protection UAVs, which will become the focus of future research. Full article

The field of Unmanned Aerial Vehicles (UAVs) has progressed greatly in the last years. UAVs are now used for many applications and are often flown automatically. One commonly implemented feature in an automatic flight is that of following a mission at a stable altitude. However, this altitude is almost always referenced from the take-off location and does not take terrain profile levels into account. This is a critical and dangerous issue because if the terrain level changes abruptly (e.g., mountain regions or buildings in a city), this can lead to crashes or an unintended (illegal) high altitude. Our aim for this work is to provide a solution such that a constant altitude above ground level is maintained. To this end, we make use of the readily available Digital Elevation Models (DEMs). These models, which contain the terrain elevation, help us in dynamically adjusting the VTOL UAV altitude so that it remains nearly constant in relation to the ground. Results have shown that with the use of our method, the altitude can be maintained sufficiently constant while introducing a limited increase in flight time and battery consumption that is proportional to the terrain’s irregularity. In a moderately changing terrain, the error could be reduced to just $\pm 5$ m. Full article

Airborne and semi-airborne transient electromagnetic (TEM) surveys have high efficiency but may suffer from systematic errors due to the inexact shape, position, and orientation of the transmitter and receiver, which can deviate from the nominal design because of complex terrain, platform instability, or external forces. Without considering actual survey geometry, modeling and inversion can bias the interpretation of results. We develop a universal approach to layered earth capable of modeling arbitrarily complex transmitter and receiver geometry used in airborne and semi-airborne surveys. Our algorithm decomposes an airborne loop or grounded wire source to a set of x-, y-, or z-oriented electric dipoles. An arbitrarily oriented receiver coil is simulated by projecting three-component data to the actual direction of receiving. In airborne TEM, the transmitter loop and receiver coil are often bound together on a rigid frame and tilt during the flight. Our simulations and synthetic inversion show that such a tilt may reduce responses relative to the data obtained with the nominal geometry; an inversion without considering the tilt can underestimate near-surface conductivity. In semi-airborne TEM, the transmitter wire on the surface can be crooked, and the airborne receiver coil can also tilt. Our modeling shows that the simulated data can change significantly if the actual transmitter and receiver geometry does not exactly follow the nominal survey design; if not appropriately accounted for, such an error may distort the recovered conductivity model. Finally, the benefit of our algorithm is demonstrated by an airborne TEM field data inversion of groundwater problems with the tilt angle of the transmitter–receiver frame accurately modeled. Our work provides a tool for improving the resolution of airborne and semi-airborne TEM in near-surface conductivity characterization. Full article