Search Results (2,506)

Vision-based unmanned aerial vehicle (UAV) detection has become increasingly important since it is a key technology in aerial collision avoidance systems. However, the detection of small UAVs is still unsatisfactory in practical applications. To address this problem, this paper proposes EWH-YOLO, a novel deep convolutional neural network-based method for small UAV detection. First, an efficient feature extraction network is designed to exact the multi-level features of small UAVs while reducing the network parameters and computational complexity. Second, a weighted bidirectional feature fusion network is proposed to enhance the low-level and high-level features in the output feature maps. Third, a hybrid bounding box regression loss is introduced to evaluate the difference between the predicted bounding box and the ground-truth bounding box during training and improve the detection accuracy. Finally, a new dataset is created on the basis of considering small UAVs to verify the detection performance. Compared with the state-of-the-art methods, the proposed method achieves higher detection accuracy with lower model complexity. The experimental results demonstrate that the proposed detector significantly improves the detection performance of small UAVs. Full article

As an emerging and powerful technology, unmanned aerial vehicles (UAVs) have tremendous potential applications in real-time monitoring, instant communication, data transmission, and more, providing ground terminal users with more efficient services and support. In this paper, we analyze the optimal networking scheme for user association with UAVs based on matching algorithms, which offer higher throughput and satisfaction. Particularly, to minimize algorithm complexity and enhance the efficiency of user devices, we propose a novel approach for stable UAV–user device pairing. This framework combines the concepts of density-based clustering algorithms and utility-driven matching algorithms. Firstly, we address the issue of large-scale scenarios with numerous and unevenly distributed user devices by proposing a clustering algorithm. This clustering algorithm divides the geographical area into multiple grids and clusters based on local density and relative distance within each grid. Next, we introduce a hierarchical matching game, where user clusters and UAVs are the players in the game. Each player ranks the other based on their individual utility functions, constructing preference lists of UAVs for users and vice versa. The network resource balancing and efficiency maximization are achieved through the matching process of bilateral selection. Simulation results demonstrate that this method exhibits low average required transmit power per user and the highest throughput among the five compared schemes under hotspot user distributions. 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

Site-specific weed management can reduce herbicide use and improve the sustainability of crop production, but its adoption remains constrained by the complexity and costs of many sensing technologies. Integrating weed detection algorithms into optical sensing systems already used for precision agriculture may represent a practical strategy to overcome these limitations. This study aimed to adapt the SWIM Weed Detection (SWIM-WD) module, originally developed for UAV imagery, to images acquired by the Augmenta Field Analyzer, a tractor-mounted optical sensor. The method was evaluated using images acquired in a commercial maize field. Calibration images acquired under weed-free conditions were used to derive crop structural parameters, whereas validation images containing weeds were used to evaluate two approaches, the Simple Method (SM) and an Improved Method (IM), the latter incorporating image-specific maize row detection. Both approaches provided Weed Green Cover (WGC) estimates showing high agreement with reference values (CCC = 0.87–0.92; R² = 0.82–0.89). The findings provide preliminary evidence that SWIM-WD can be adapted to a tractor-mounted sensing platform, supporting precision agriculture systems with weed detection capabilities without dedicated hardware. Further validation across fields, crop stages, and environmental conditions is required to assess the robustness and general applicability of the method. 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

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

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

Litchi (Litchi chinensis Sonn.) is an economically important tropical and subtropical fruit crop, but frequent outbreaks of diseases and pests severely threaten yield and quality. Traditional field monitoring is inefficient and cannot meet requirements for early and accurate detection over large areas. Although various sensing technologies and artificial intelligence (AI)-based methods have been developed, the literature remains fragmented and lacks systematic comparison across different monitoring scales and technological approaches. This review summarizes recent advances in intelligent detection technologies for litchi diseases and pests across scales ranging from individual fruits to entire orchards. First, biological and spectral response mechanisms of infected tissues are introduced as a theoretical basis. Then, fruit-level sensing technologies, including near-infrared spectroscopy, multispectral and hyperspectral imaging, RGB imaging, fluorescence sensing, and data fusion, are reviewed. Orchard-scale monitoring using unmanned aerial vehicles (UAVs) and Internet of Things (IoT) is further analyzed. Machine learning and deep learning methods for feature extraction, recognition, and risk prediction are also summarized. Finally, advantages, limitations, and application scenarios of different technologies are compared in terms of their advantages, limitations, and suitable application scenarios. This review highlights the transition toward multimodal and intelligent monitoring systems and discusses future directions including edge intelligence, multimodal fusion, and collaborative monitoring for precision agriculture. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

Accurately extracting the spatial distribution of shrubs is an important basis for scientific diagnosis, rational prevention, and control of shrub-encroached grasslands (SGs). In SGs, the landscape exhibits a high degree of spatial intermingling among shrubs, herbaceous vegetation, and bare soil, resulting in a serious issue of mixed pixels. The combination of UAV hyperspectral imaging and deep learning provides the most promising technical approach for shrub–grass separation at present. However, issues such as data redundancy and algorithmic adaptability urgently need to be addressed. In this study, Caragana microphylla Lam, a typical shrub species found in the SGs of Inner Mongolia, is the subject for extraction. This work aims to extract sensitive spectral bands from UAV hyperspectral data, construct a deep learning-based framework for shrub identification, and map the distribution of Caragana microphylla Lam in the study area. First, a dataset of hyperspectral images in Xilinhot City, Inner Mongolia, China, was constructed by manual visual interpretation for shrub identification. Second, a novel unsupervised band selection algorithm, US-BS-Net, was proposed to optimize the proxy task of BS-Conv-Net by introducing contrastive learning. It can be used to obtain sensitive spectral bands with discriminative feature representations. Finally, the US-BS-P-Net framework was proposed by combining the US-BS-Net with the prototypical network to construct high-precision shrub identification models in small-sample scenarios. Taking 80 bands as input, the highest overall accuracy reaches 93.15%, which is better than the full spectrum and other deep learning models. The deep synergy between hyperspectral technology and deep learning effectively overcomes traditional challenges such as spectral similarity between shrubs and grasses and their mixed spatial distribution. In particular, the proposed US-BS-P-Net, which combines the advantages of US-BS-Net in sensitive band extraction and the prototypical network in few-shot model construction, provides excellent fundamental data for precise monitoring of SGs. Full article

Trajectory planning is a key enabling technology for UAV swarms operating in complex and obstacle-rich environments. This paper proposes a distributed nonlinear model predictive control (NMPC)-based trajectory planning method for UAV swarms, where terminal target reaching, prescribed formation maintenance, obstacle avoidance, and inter-UAV collision avoidance are incorporated into a unified predictive optimization framework. To reduce the online computational burden, a control parameterization strategy is introduced to describe the control input using M control segments, thereby reducing the dimension of the online decision variables. Furthermore, an exact-penalty-based constraint transcription method is developed to transform the original constrained optimal control problem into a lower-complexity finite-dimensional nonlinear programming problem, while efficiently handling velocity constraints, obstacle avoidance constraints, and inter-UAV collision avoidance constraints. Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error. Furthermore, real UAV swarm flight experiments were conducted to further validate the practical feasibility and online applicability of the proposed distributed NMPC framework for UAV swarm trajectory planning. Full article

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton. Full article

Despite the rapid proliferation of unmanned aerial vehicles (UAVs) across industrial, agricultural, and scientific domains, their deployment remains constrained by limited endurance, aerodynamic inefficiency, and acoustic emissions, all mediated by a shared onboard energy budget. Existing biomimetic UAV reviews have generally treated aerodynamics, structures, sensing, control, and energy systems as parallel topics rather than as interacting components of a unified aerial architecture. Drawing primarily on literature published between 2015 and June 2026 and identified through searches of Web of Science, Scopus, and Google Scholar, this review addresses this gap by examining bio-inspired technologies across six principal domains: aeroacoustic and passive flow control, aerodynamic efficiency, multifunctional structural composites, neuromorphic sensing and control, ionic energy storage, and energy harvesting. Its principal contribution is a cross-domain synergy analysis identifying five performance couplings and one structural enabling architecture through which these domains interact physically and functionally. Representative examples include serration-based propeller geometries that can simultaneously reduce noise and power demand; morphing wing surfaces that serve as both aerodynamic structures and triboelectric harvesting substrates; and neuromorphic spiking neural networks that have been reported, in specific event-vision inference benchmarks, to reduce inference energy by three to four orders of magnitude relative to embedded graphics processing unit (GPU)-based implementations. Mechanical harvesting outputs nonetheless remain orders of magnitude below propulsion requirements and are thus positioned as supplementary. Four systemic barriers (unquantified mass–energy balance, undocumented durability, aeroelastic co-design gaps, and heterogeneous metrics) are evaluated, and the resulting synthesis indicates that advancing bio-inspired UAVs requires a transition from structural imitation to functional, system-level biomimetics. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has introduced increasingly severe airspace security challenges. As a critical component of counter-UAV systems, the direction-finding (DF) accuracy of airborne warning systems directly affects threat assessment and response efficiency. This paper addresses the problem of limited DF accuracy in airborne environments by proposing a direction-finding method that integrates attitude self-calibration with real-time signal-to-noise ratio (SNR) estimation. Based on the monopulse amplitude–phase comparison angle-measurement principle, the proposed method dynamically corrects the angle-discrimination curve using real-time SNR information and adaptively calibrates azimuth information by incorporating UAV attitude data. Simulation and experimental results demonstrate that the proposed method significantly reduces angle-measurement errors under low-SNR conditions, and attitude calibration further improves DF accuracy across the full angular range. Field experiments indicate that, after attitude calibration, the angle-measurement error is less than $2^{\circ }$ in over $77\%$ of the test points. Full article