Search Results (9,349)

Unmanned aerial vehicle (UAV) remote sensing can reveal spatial variability in maize, but its value depends on whether observations support timely and reliable management. This structured critical review synthesizes UAV applications from stand establishment and canopy development to water and nutrient assessment, stress monitoring, yield prediction, and decision support. The evidence corpus comprised 82 sources, including 51 core maize–UAV studies, evaluated by growth stage, validation strength, and operational endpoint. RGB, multispectral, hyperspectral, thermal, and three-dimensional methods provide complementary information for plant counting, canopy traits, treatment-related water and nitrogen responses, visible stress mapping, and within-experiment yield variation. Most evidence, however, comes from experimental or site-specific settings. Independent testing across sites, years, cultivars, and production environments remains uncommon, as do physiological confirmation of interacting stresses and translation of diagnostic maps into machinery-ready operations. UAV sensing should therefore be viewed as complementary to satellite observations and field scouting, with its advantage determined by target, scale, timing, and decision requirements. Future research should prioritize phenology-aware acquisition, independent field validation, uncertainty relative to management thresholds, interoperable prescription and machinery data, and closed-loop evaluation of input use, crop response, yield, and economic return. These steps are needed to move from high-resolution mapping toward reproducible precision management in maize. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Reservoir impoundment can reactivate pre-existing landslides and reorganize slope topography, thereby changing seepage conditions and subsequent deformation. However, crack mapping, geomorphic interpretation, and hydrodynamic diagnosis are still often treated as separate tasks. This study investigates the Hemenkou (HMK) landslide in the Wudongde Reservoir area, China, using multi-scale space–air–ground observations, including multi-temporal optical satellite images, unmanned aerial vehicle (UAV) photogrammetry, pyramid scene parsing network (PSPNet)-based crack segmentation, global navigation satellite system (GNSS) monitoring, and convergent cross mapping (CCM). The remote sensing record shows a progressive damage sequence: cracks were mainly restricted to the upper source area in 2012, crown cracking intensified and propagated downslope by December 2020, and the UAV survey of 10 June 2024 revealed a mature tension-crack network concentrated in Zone II. ResNet-50-PSPNet achieved the best crack-extraction performance among the tested models, with Precision = 0.9120, Recall = 0.9041, F1 = 0.9081, and IoU = 0.8316. The mapped cracks are dominated by short, narrow, northeast–southwest-oriented tension cracks. GNSS monitoring reveals strong spatial heterogeneity, with stepwise deformation concentrated in Zone II. CCM provides strong directional evidence for the influence of reservoir water-level fluctuation on Zone II deformation, whereas the weaker rainfall signal is consistent with a secondary reinforcing role. The apparent increase in the rainfall-related CCM signal from 2021 to 2023 is consistent with progressive crack expansion and potentially enhanced hydraulic connectivity in Zone II. Taken together, these observations support the interpretation that post-deformation topography, particularly the tension-crack network and disturbed toe, may organise preferential seepage pathways and increase the sensitivity of the landslide to reservoir drawdown. The study provides an integrated remote sensing and monitoring framework for process-based interpretation of reservoir landslides. Full article

The use of uniform spray volume rates for fruit trees with different canopy sizes is common in orchard spraying with plant protection unmanned aerial vehicles (UAVs), whereas the applicability of Leaf Wall Area (LWA)- and Tree Row Volume (TRV)-based methods to UAV spraying remains insufficiently validated. This study evaluated canopy size-based spray volume optimization and droplet deposition and penetration along the vertical canopy profile in a mountainous Nanguo pear orchard. The results showed that, under a uniform spray volume rate, small canopy trees exhibited significantly higher droplet deposition and ground deposition than large canopy trees, indicating greater potential spray losses. LWA- and TRV-based adjustment reduced the spray volume rate for small canopy trees by 43.0% and 49.0%, respectively, while maintaining comparable deposition in the upper and middle canopy layers. However, deposition in the lower canopy and on abaxial leaf surfaces remained limited, indicating that conventional LWA and TRV methods do not fully account for the top-down deposition characteristics of UAV spraying. Along the vertical canopy profile, finer atomization levels generally favored droplet penetration into the lower canopy, whereas increasing the spray volume rate increased overall deposition but did not significantly improve vertical penetration. Flight speed showed no consistent effect on penetration within the tested range. The results highlight canopy size as a key factor in UAV spray deposition. Canopy size-based variable-rate application can reduce spray volume rate while maintaining effective deposition, but further optimization should consider rotor-induced airflow and canopy structure. Full article

Using a balanced panel of 30 Chinese provinces from 2018 to 2024, this study examines the association between the crop-protection unmanned aerial vehicle (UAV) service area and the agricultural insurance claims paid-to-premium income ratio. The outcome is interpreted narrowly as annual paid-claims burden relative to premium income, not as an actuarial incurred loss ratio or a measure of profitability, solvency, or sustainability. Two-way fixed-effects models with province-clustered standard errors show that an additional 100,000 hectares of UAV service area is associated with a 1.07-percentage-point lower ratio. Decomposition regressions show a negative association with log claims paid (β = −0.0153, p = 0.040) but no significant association with log premium income (β = −0.0020, p = 0.787). The association remains negative across robustness checks, with weaker evidence for the lagged specification. In 10,000 within-year province-reassignment placebo draws, the observed coefficient is extreme relative to the placebo distribution (randomization p = 0.0001). A clustered bootstrap is consistent with a broad multi-hazard pathway rather than causal mediation. Formal tests show that detectable regional differences are concentrated in comparisons involving the Western region. Given the observational design, the findings document associations rather than causal effects. Full article

This paper develops a distributed control framework for leader–follower formation tracking in multi-agent unmanned aerial vehicle (UAV) systems. Feedforward compensation converts the networked tracking task into local error stabilization problems, and an L_p zero-sum game is used to construct a finite-time robust feedback law. The disturbance-free closed loop is proven to converge in finite time, whereas under nonzero disturbances, the result is a certified L_p attenuation bound rather than exact finite-time convergence. A single-critic adaptive dynamic programming architecture approximates the value function, and an offline sampled data training procedure avoids injecting probing noise into the physical plant. In the reported planar outer-loop simulation, the local errors settle within 4.3 s, compared with 8.2 s for the quadratic L₂ baseline, and the reported cumulative disturbance attenuation indicator decreases from 2.74 to 0.48. The current validation uses a fully actuated translational outer-loop abstraction; extensions to underactuated six-degree-of-freedom dynamics, saturation, and hardware experiments are left for future work L_p. Full article

Automatic detection of grassland livestock from unmanned aerial vehicle (UAV) imagery can support livestock resource surveys and grazing management. However, compared with larger livestock such as cattle and horses, sheep in wide-field images are typically small, densely distributed, closely spaced, and easily confused with similar background textures, resulting in missed detections, background false positives, and duplicate detections. To address these challenges, this study presents DAFS-YOLO for UAV-based livestock detection in dense sheep scenes. A Dense-Aware Shallow Convolution module (DASConv) enhances shallow-feature discriminability for small-scale sheep in dense scenes, reducing missed detections and background false positives. A Local Spatial–Semantic Complementary Mapping module (LSCM) preserves richer shallow spatial information during feature propagation, improving small-object localization. Gaussian Soft-NMS optimizes the selection of highly overlapping candidate boxes in dense sheep regions and reduces duplicate detections. Experiments on a self-constructed UAV livestock dataset show that DAFS-YOLO achieves ${\mathrm{m}\mathrm{A}\mathrm{P}}_{50}$ and ${\mathrm{m}\mathrm{A}\mathrm{P}}_{50:95}$ values of 0.930 and 0.634, outperforming YOLOv11n by 4.4 and 4.8 percentage points, respectively. The corresponding sheep-class values are 0.922 and 0.564. With only 2.65 M parameters, the model also demonstrates good cross-dataset generalization on SheepCounter, providing an effective solution for intelligent UAV-based livestock monitoring. Full article

Rock slope landslide potential, tunnel design, and the engineering of underground spaces critically require an analysis of rock joint orientation and spacing. Joint set measurements are typically determined by hand in the field using a compass–clinometer to measure the orientation of the geologic planes with respect to north and the dip of the plane with respect to the horizontal. Unmanned aerial vehicles (UAVs) can be utilized to capture hundreds of photos to create high-resolution 3D models of a rock outcrop, capturing visible joint set discontinuities on a faceted surface of a triangular irregular network (TIN). Computing methods of facets and facet normals from a point cloud allow for the characterization of discontinuity orientations without the need for manual measurements in the field. However, these methods used to calculate facets and facet normals result in the addition of noise in the dataset, which increases the difficulty of analysis. A two-stage filtering process employing density-based spatial clustering of applications with noise (DBSCAN), and the second derivative of the remaining clusters, removes data that are not representative of a discontinuity within the intact rock mass. Finally, an unsupervised clustering algorithm, K-Means Clustering, is applied to the dataset to extract the dip and dip direction of discontinuities. The methodology used to identify the orthogonal joint sets on a dam spillway demonstrated good performance across all identified joint sets, with the estimated variability in dip and dip direction broadly comparable to that observed in the manual field dataset. This indicates that this newly developed proof-of-concept approach can reliably capture the orientation and variability of orthogonal joint sets from large datasets. Full article

Driven by the escalating global agricultural workforce shortage and the urgent need to meet the “Zero Hunger” mandate, the automation of harvest–transport workflows has emerged as a cornerstone of Agriculture 4.0. This paper highlights the latest research progress in multi-robot collaborative logistics scheduling across highly unstructured farming environments, underpinned by cutting-edge spatial perception and digital twin frameworks. Initially, we summarize the technological leap from conventional 2D geometric mapping to multi-modal semantic 3D reconstruction—fusing light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) imagery, and spatial data—to enable high-fidelity forward-looking predictions. The discussion then transitions to algorithmic advancements, emphasizing the shift from traditional centralized operations research to decentralized, data-driven approaches such as Multi-Agent Reinforcement Learning (MARL). We also explore micro-kinematic predictive control mechanisms and the growing integration of ecological sustainability metrics into routing models. To demonstrate practical engineering progress, multi-agent implementations are analyzed across three typical spatial settings: high-throughput continuous relays in open fields, global navigation satellite system (GNSS)-denied discrete routing in dense orchards, and close-proximity human–robot collaboration (HRC) in smart greenhouses. Finally, we identify the remaining barriers to the large-scale commercialization of Agricultural Cyber-Physical Systems (ACPS), such as the “Sim-to-Real” gap restricted by edge-computing capacities, unclosed economic loops, and HRC ethical dilemmas, offering a forward-looking roadmap for next-generation resilient agricultural networks. Full article

The present study experimentally investigates the influence of n-octanol addition to Jet-A fuel on the combustion performance and emission behavior of a micro-turboprop-based hybrid UAV (“Unmanned Aerial Vehicle”) power system. The experiments were conducted on a dedicated hybrid propulsion test bench equipped with a KingTech micro-turboprop engine mechanically coupled to a T-Motor electric generator and supplying a regulated 48 V DC bus. The system is capable of delivering approximately 3 kW of continuous electrical power, with peak values reaching 3.5 kW. Jet-A and three n-octanol/Jet-A blends containing 10%, 20%, and 30% n-octanol by volume, denoted O10, O20, and O30, respectively, were tested under four operating regimes ranging from idle to 2500 W electrical load. Exhaust gas temperature, carbon monoxide, sulfur dioxide, nitrogen oxides, electrical output, and near-field pollutant dispersion were evaluated. The results show that n-octanol addition affects engine behavior in a strongly load-dependent manner. At idle, the O10 blend reduced CO concentration from approximately 2520 ppm for Jet-A to approximately 2270 ppm, corresponding to a reduction of about 9.9%. At the same operating condition, O10 reduced exhaust gas temperature from approximately 498.3 °C to 463.2 °C, while O20 and O30 produced stronger cooling effects. At intermediate regimes, the oxygenated molecular structure of n-octanol contributed to lower CO formation in selected cases, indicating improved combustion-completeness behavior. At high load, however, exhaust gas temperatures converged toward or exceeded those of Jet-A, particularly for O30, showing that higher octanol fractions may introduce additional thermal constraints. Among the tested fuels, O10, corresponding to 10% n-octanol by volume, provided the most balanced behavior across the investigated operating range, from idle to 2500 W electrical load. The dispersion measurements performed at 30 m from the source further showed that ambient pollutant concentrations are strongly influenced by wind speed, wind direction, and plume transport. These findings support moderate n-octanol blending as a promising transitional strategy for small-scale hybrid UAV propulsion systems, while highlighting the need for future repeated testing, direct fuel-flow measurement, and numerical dispersion modeling. Full article

Unmanned aerial vehicle (UAV) remote sensing object detection remains challenging because aerial targets are often small, densely distributed, and embedded in complex background clutter, whereas onboard deployment requires compact computation and real-time inference. Existing real-time detectors commonly retain a conventional $P_3$–$P_5$ detection hierarchy, in which deep low-resolution stages may consume computation after fine-grained small-object cues have already been weakened. To address this issue, this paper presents feature-modulated and redundancy-suppressed YOLO (FMRS-YOLO), a UAV-oriented lightweight detector that improves the accuracy–efficiency trade-off through the redundancy-reduction (RR) scale allocation strategy and targeted feature refinement. Instead of extending the terminal hierarchy to the conventional $P_5$ scale, FMRS-YOLO replaces the original stride-2 $P_5$ transition with a stride-1 high-level transformation and omits the corresponding $P_5$ prediction branch, thereby concentrating detection on the $P_3$ and $P_4$ feature levels where small aerial targets retain more informative spatial cues. To enhance feature representation under the compact RR scale allocation framework, we propose Gaussian cross-feature modulation (GCFM) to strengthen spatial–semantic interaction, and design attention-weighted parallel pyramid fusion (AWPPF) to improve clutter-aware multi-scale aggregation while preserving unpooled spatial details. Experiments are conducted separately on VisDrone-2019, a drone-based object detection benchmark used as the primary benchmark, and UCAS-AOD, an aerial object detection benchmark for aircraft and cars used as an independent complementary benchmark. Compared with scale-matched YOLOv11 baselines on VisDrone-2019, FMRS-YOLO improves mAP by 1.9–2.4 percentage points and mAP${}_{50}$ by 2.3–2.9 percentage points, while reducing parameters by 41.0–64.9%, reducing FLOPs by 1.6–20.7%, and increasing FPS by 9.3–10.4%. On UCAS-AOD, FMRS-YOLOn achieves 71.3% mAP, 98.8% mAP${}_{50}$, and 88.3% mAP${}_{75}$, showing favorable localization performance under a compact model scale. Ablation and visualization results further indicate that the proposed architecture improves foreground focus, suppresses background interference, and strengthens small-object localization. These results demonstrate that FMRS-YOLO provides a practical accuracy–efficiency trade-off for UAV remote sensing object detection. Full article

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. This paper presents a Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model. The UAV acts as an aerial decode-and-forward relay between IoT devices and a Base Station (BS), with a Deep Q-Network (DQN) making decisions based on queue backlogs, channel conditions, UAV position, and remaining battery. The reward function balances Energy Efficiency (EE), queue stability, fairness, and battery longevity. We benchmark the DQN against six baselines; Round Robin (RR), Random Allocation (RA), the Single-to-Noise Ratio (Max-SNR), Proportional Fair (PF), a Lyapunov heuristic, and a GreedyEE scheme; across a range of device counts, traffic loads, battery budgets, and flight altitudes. Simulations consistently show that the DQN outperforms all baselines, including a RA baseline with equal access to UAV mobility; in EE, throughput, delay, and fairness, confirming that the gain stems from the learned joint control policy rather than from UAV mobility being available. Full article

Pasture quality monitoring is essential for optimizing grazing management and reducing the incidence of nutritional and metabolic disorders in ruminants, yet conventional field-based measurements remain labor-intensive and limited in spatial coverage. This review examines how remote sensing (RS) technologies can support pasture-based livestock systems by providing timely, scalable assessments of biomass, botanical composition, and nutritive attributes. Data from multispectral, hyperspectral, radio detection and ranging (RADAR), and light detection and ranging (LiDAR) sensors, acquired via satellite, unmanned aerial vehicle (UAV), and proximal platforms, are combined with machine learning (ML) methods and radiative transfer models to derive pasture biophysical and quality indicators. The reviewed evidence shows that RS reliably estimates pasture biomass and structural traits, while advances in spectral unmixing, data fusion, and artificial intelligence (AI) improve the characterization of heterogeneous swards and support emerging indicators related to forage quality. Integrating these remotely sensed metrics into grassland decision-support frameworks can enhance grazing allocation, inform fertilization and irrigation decisions, and help detect conditions associated with nutritional imbalances. Overall, the synthesis demonstrates that RS, particularly when combined with advanced modelling and cloud-based processing, offers a robust pathway for improving pasture monitoring and strengthening the nutritional management of ruminants, thereby supporting more sustainable and animal welfare-focused grazing systems. Full article

The actuator plays a crucial role in controlling the flight attitude of unmanned aerial vehicles (UAVs), making timely anomaly detection essential for operational reliability and flight safety. However, existing methods often have difficulty jointly modeling the complex temporal dynamics and inter-variable spatial dependencies of multivariate actuator signals, while their computational complexity can limit real-time deployment. Moreover, most existing approaches rely on univariate or weakly coupled representations, making them less effective in detecting simultaneous faults across multiple actuators. To address these challenges, this paper proposes a Multivariate Spatio-Temporal Feature Fusion Transformer (STF_Tran) framework for anomaly detection in fixed-wing UAV actuators. Unlike conventional transformer-based multivariate anomaly detection methods that employ a shared attention mechanism to model heterogeneous dependencies, STF_Tran adopts a dual-branch architecture that separately encodes temporal dynamics and spatial correlations from multivariate actuator signals. A self-learning mechanism enables each branch to learn discriminative representations directly from normal operating data without requiring explicit fault labels, while a feature fusion module integrates the complementary spatio-temporal representations for anomaly reconstruction and scoring. Faults are identified by comparing the resulting anomaly scores with predefined thresholds, enabling the detection of diverse and simultaneous actuator anomalies. Experimental results demonstrate the effectiveness of STF_Tran, achieving F1 scores of 0.9944 and 0.9949 for deviation and stuck anomalies, respectively, and consistently outperforming several state-of-the-art methods. Full article

Accurate radar tracking of highly maneuverable unmanned aerial vehicles (UAVs) is a key enabling technology for low-altitude airspace surveillance, counter-UAS defense, and UAS traffic management (UTM). Once a non-cooperative UAV has been detected, estimating its motion state must cope with nonlinear polar-coordinate observations, unknown maneuver-mode switching, and multi-scale state variations driven by agile drone flight, making it difficult for classical IMM/UKF filters and deep sequence models to preserve local maneuver response and long-term temporal consistency. We propose MSGate, a multi-scale gated temporal network organized along an “observation-representation-fusion-constraint” pipeline. A non-learnable physical front end maps polar measurements into a Cartesian observation trajectory of the same dimension as the UAV state. Multi-scale gated convolution and RoPE-Transformer encoding extract local maneuver responses and long-range dependencies. A shared gated dual-path decoder fuses the two paths adaptively at each time step and channel, and velocity-smoothness and position-velocity kinematic consistency terms regularize the predicted trajectory. On the real-UAV datasets UZH-FPV, EuRoC MAV, and NeuroBEM, under a unified range-azimuth observation protocol, MSGate attains the lowest average position and velocity errors (Pos-RMSE $0.0486\mathrm{m}$; Vel-RMSE $0.1767\mathrm{m}/\mathrm{s}$), outperforming the strongest time-series baseline TimeMixer, and generalizes to a separate nano-quadrotor dataset (NanoBench). MSGate provides an accurate, maneuver-robust solution for radar state estimation of highly maneuverable UAVs. Full article