Search Results (4,818)

The use of nondestructive technologies combined with machine learning has emerged as a promising approach for estimating structural and productive traits in agricultural systems. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) imagery integrated with the Random Forest algorithm to predict structural, physiological and productive variables of forage cactus cultivated under semi-arid conditions. The experiment was conducted over two years using four varieties: Orelha de Elefante Mexicana (OEM), Miúda, IPA Sertânia and IPA 20. RGB and red–green–near-infrared (RGNir) orthomosaics, along with a digital elevation model, were used to derive spectral and structural variables, which were related to field measurements. Model performance was assessed using the coefficient of determination (R²). The models showed high predictive performance for dry mass production, particularly for OEM, IPA Sertânia and IPA 20 (R² = 0.85, 0.85 and 0.83). Physiological variables, such as chlorophyll A and B, also showed consistent fits (R² = 0.70 and 0.83), while structural variables, including height and volume, exhibited lower stability. Differences among varieties affected model accuracy, especially for Miúda, due to its architectural characteristics. The integration of UAV imagery and machine learning provides a reliable approach for monitoring forage cactus, although model performance depends on plant structure. Full article

Single-photon detection is a promising approach for low–slow–small Unmanned Aerial Vehicle (UAV) detection, holding great value in urban air defense and security monitoring. In complex urban environments, intense non-uniform building clutter and multi-echo aliasing easily submerge weak target signals, severely limiting traditional single-photon systems under low signal-to-background ratios. To address this, this paper proposes an urban-oriented detection strategy based on a free-running single-photon array, and designs a dual-optimized pulse position modulation laser detection and range image enhancement algorithm. By establishing temporal correlations via pulse sequence convolution, the algorithm effectively isolates weak UAV echoes from strong background clutter to break through detection limitations. Compared with the popular Markov correction method that often suppresses overlapping weak targets under strong reflections, the proposed method significantly improves small-target feature retention, successfully balancing background elimination and detection sensitivity. Field tests and quantitative evaluations demonstrate that the system reliably eliminates building clutter and achieves stable continuous tracking of weak UAV signals within 1.5 km, providing a highly robust and effective technical solution for urban low-altitude surveillance. Full article

This study presents a reusable adjoint-octree multilevel fast multipole algorithm (MLFMA) for full-wave radar scattering analysis of multi-state unmanned aerial vehicle (UAV) formations. The method is motivated by remote-sensing applications in which dense angular sampling or long motion sequences are required for physically reliable signature generation. Instead of rebuilding a global octree for the full formation at every motion state, the proposed approach assigns each sub-target an independent target-attached local octree that translates and rotates with the rigid body. This preserves mesh–cell affiliation in the body-fixed frame and separates the system operator into a state-invariant intra-target near-field component and a state-dependent inter-target far-field component. Consequently, near-field matrices and sparse approximate inverse preconditioners are assembled once and reused throughout the state sequence, while only inter-target far-field coupling terms are updated. The method is evaluated for six representative UAV formations at 3.5 GHz using monostatic radar cross section (RCS) over a full azimuth sweep. Across all tested formations, the proposed solver reproduces the RCS behavior of conventional MLFMA while substantially reducing computational cost. For Formation A, the center-state total time decreases from 251.4 s to 66.06 s; for Formation C, it decreases from 470.95 s to 76.06 s. Over 100-state sequences, the resulting acceleration reaches approximately 11.8-fold and 15.2-fold, respectively. Jitter-envelope analysis further shows that orientation perturbation produces stronger signature uncertainty than planar displacement. The proposed framework therefore provides an efficient and physically consistent forward solver for radar remote-sensing studies of cooperative UAV formations. Full article

To satisfy the demands of 5G communication and reliable data connectivity for unmanned aerial vehicles (UAVs), a novel two-element dual-band MIMO antenna with an inherent self-decoupling property based on orthogonal linear polarization diversity is proposed. Distinct from conventional designs relying on extra decoupling components, the antenna realizes isolation enhancement via coupled currents between annular strips and S-shaped strips without additional decoupling structures, representing the core design novelty. Fabricated on a low-cost 1.6 mm thick FR4 substrate, the antenna features compact overall dimensions of 60 mm × 30 mm × 1.6 mm, covering the 2.40–2.73 GHz ISM band and 3.38–3.63 GHz 5G Sub-6 GHz band. Measured results demonstrate that the reflection coefficient remains below −10 dB across the entire operating bands, with port isolation exceeding 27 dB for the 2.4 GHz band and 20 dB for the 3.5 GHz 5G band. The measured realized gain is 0.7–1.5 dB in the lower band and 2.3–2.9 dB in the upper band. The radiation efficiency, which is obtained exclusively from ANSYS HFSS 2025 R1 simulation, is higher than 90% for the lower band and over 80% for the upper band. The calculated envelope correlation coefficient (ECC) is less than 0.15 throughout the working bandwidth, which effectively suppresses inter-channel electromagnetic interference and mitigates channel fading caused by varying UAV attitudes to improve system channel capacity. Further verifications via epoxy encapsulation and co-simulation on an eight-rotor UAV platform prove slight frequency drift after packaging and installation, whereas its bandwidth and isolation still meet practical engineering requirements. Benefiting from a compact layout and omnidirectional radiation performance, the proposed low-cost MIMO antenna is convenient for conformal integration into a UAV fuselage, improving the practicability of UAV-aided emergency communication, equipment inspection and 5G network coverage. Full article

The escalating security risks associated with unauthorized unmanned aerial vehicles (UAVs) in advancing smart cities necessitate the development of robust active countermeasures. This work presents a novel approach centered on a UAV-borne flexible net system and provides a rigorous investigation into its complex nonlinear dynamics. This study establishes a lumped-mass, semi-spring–damper dynamic model of the flexible capture net, characterizing its key dynamic properties, including deployment performance, aerodynamic attitude, and the high-impact phenomena of collision and entanglement with the target UAV. To verify the reliability of the proposed method, numerical simulations are combined with field tests for systematic validation. Comparative analysis reveals excellent quantitative agreement, with over 80% conformity in the net’s spatial configuration between simulated and experimental results. This paper illuminates the fundamental principles governing energy dissipation and transient tension dynamics pre- and post-capture. This study provides preliminary evidence for the feasibility of the proposed method and identifies key directions for future investigation. The findings offer guidance for the design and optimization of future systems intended to neutralize low, slow, and small (LSS) aerial threats. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

Point-cloud analysis of sedimentary outcrops using Unmanned Aerial Vehicle (UAV) oblique photogrammetry is a crucial approach to sedimentary system characterization, stratigraphic correlation, and petroleum exploration analog studies. In large-scale field settings, however, outcrops are often scattered and fragmented, vegetation and soil cover is extensive, and class imbalance is pronounced. Manual interpretation is labor-intensive, while existing clustering algorithms, conventional machine learning methods, and general-purpose point-cloud segmentation networks struggle to simultaneously ensure geometric fidelity, rare-class recognition, and multi-scale feature integration. To address these challenges, we propose a method for extracting sedimentary outcrop point clouds from field surface point clouds using a UAV oblique photogrammetry acquisition strategy. The core segmentation module of the method, sedimentary cross-scale self-attention network (SedCSA-Net), is an enhanced version of PointNet++ that integrates collaborative improvements across four dimensions: data augmentation, sampling strategy, feature encoding, and loss optimization. Taking the Cretaceous Qingshuihe Formation in the Louzhuangzi area of the southern Junggar Basin as a case study, our experimental results indicate that SedCSA-Net overcomes the natural variability of UAV oblique photogrammetry point clouds—such as shadows, voids, and uneven density—achieving a mean Intersection over Union(mIoU) of 89.51% and an Overall Accuracy(OA) of 96.08%, with an outcrop-class Intersection over Union(IoU) of 86.90%. Attitude measurements derived from segmentation results deviate by less than 3° from manually annotated references, demonstrating that the proposed framework provides an end-to-end, generalizable approach for intelligent segmentation, geometric reconstruction, and attitude extraction of large-scale sedimentary outcrop point clouds. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

Complex urban scenarios impose high demands on the environmental perception capabilities of unmanned systems, which serve as a prerequisite for executing autonomous missions such as disaster response, infrastructure inspection, and smart city operations. UAVs, leveraging their high mobility, can provide accurate prior maps and wide-area aerial observation for unmanned ground vehicles. However, their long-range perception accuracy is limited. Conversely, UGVs can achieve high-precision environmental perception along their navigation paths using prior maps, but suffer from a constrained field of view. The collaboration between the two platforms complements their respective strengths, thereby enhancing 3D object perception and mapping accuracy in complex scenarios. To address the aforementioned challenges, this study proposes a cross-platform feature fusion method for 3D object perception and an incremental map updating approach for UAVs and UGVs. First, a dynamic SLAM method that integrates an optimized YOLOv8 with ORB-SLAM3 is employed to mitigate map blurring caused by dynamic noise, providing prior map information for UGVs. Second, a multimodal fusion perception model is constructed for UGVs, utilizing attention mechanisms to achieve deep fusion of multimodal Bird’s-Eye-View (BEV) features. This overcomes issues such as diminishing complementarity between modalities and weak temporal feature associations. Finally, an air ground fusion model based on a cross-attention mechanism is developed to fuse aerial view features with ground-based fused BEV features across platforms, yielding a unified feature representation for 3D object detection and generating a fused high-precision map. Experimental results demonstrate that under complex occlusion scenarios in a simulated dataset, the proposed collaborative perception system improves the mean Average Precision (mAP) by 12.7% and 15.7% compared to using a single UAV or a single UGV, respectively, while increasing the map accuracy F1-score by 0.21. This study provides technical support for achieving real-time and accurate air ground collaborative perception in complex dynamic environments. Full article

Mining-subsidence-induced surface cracks pose substantial risks to ecological systems, infrastructure stability, and mining safety. Their thin, elongated, discontinuous, and low-contrast characteristics make accurate detection from unmanned aerial vehicle (UAV) imagery challenging, particularly under complex environmental conditions. This study proposes an enhanced YOLO11n framework for detecting surface cracks in mining subsidence areas. Switchable Atrous Convolution (SAConv) was incorporated to strengthen multi-scale feature extraction, while Cascaded Group Attention (CGA) was introduced to suppress background interference and improve feature discrimination, and Shape-IoU loss was adopted to enhance the localization of slender crack targets. The model was evaluated using 5000 annotated UAV images collected in the Zhungeer mining area. It achieved a precision of 85.6%, a recall of 77.9%, an mAP@0.5 of 84.3%, and an F1-score of 81.6%. Compared with the baseline YOLO11n, precision, recall, and mAP@0.5 increased by 1.4, 4.6, and 3.2 percentage points, respectively. Cross-dataset evaluation on the public Crack500 dataset further demonstrated improved robustness under domain variation. These results indicate that the proposed framework improves the detection and localization of slender and discontinuous cracks in complex mining environments, supporting its application in UAV-based geological hazard monitoring. Full article

The Vortex Ring State (VRS) poses a catastrophic aerodynamic threat to coaxial dual-rotor unmanned aerial vehicles (UAVs). Traditional reactive detection mechanisms provide insufficient altitude for recovery, while existing data-driven diagnostics are severely bottlenecked by data leakage, extreme class imbalance, and a lack of physical interpretability. To bridge these gaps, this paper proposes a physics-informed multimodal deep learning framework that transitions from post-occurrence detection to proactive early warning. We establish a 1.5 s precursor window—creating a three-class ordinal state space—to provide the flight control system with critical intervention time for differential rotor recovery. We developed a novel ViT-LSTM architecture (MTSF-Net) to fuse continuous seven-channel onboard-recorded data (comprising three-axis acceleration, three-axis angular velocity, and barometric vertical velocity), which are subsequently transformed into Continuous Wavelet Transform (CWT) spectrograms. To ensure real-time unidirectional inference while preserving absolute physical vibration scales across heterogeneous sensors, a Calibrated Benchmark Normalization (CBN) strategy is introduced. Furthermore, a Hybrid Ordinal Loss is proposed to mitigate the extreme sample imbalance (<0.5%) of the precursor state by penalizing asymmetric aerodynamic degradation. Evaluated under a strict sortie-based isolation protocol, the proposed system achieves an exceptional test accuracy of 98.26% and an unprecedented precursor recall of 100%. Notably, it completely eliminates fatal missed detections (VRS predicted as Normal) and false-positive VRS predictions triggered by precursor states. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to verify that the multimodal sensor processing pipeline successfully anchors onto authentic physical vibration frequencies rather than artifactual noise, laying a rigorous, interpretable foundation for intelligent aviation safety systems. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article