Search Results (3,210)

The present study investigated the detection performance of the YOLOv8s, YOLO11s, and YOLO12s models, implemented within convolutional neural network architectures, for identifying floricane raspberry (Rubus idaeus L.) shrubs using RGB imagery and multispectral data acquired in the near-infrared, red-edge, red, and green spectral bands with a DJI Mavic 3 Multispectral drone. Model training and validation were conducted to evaluate both within-modality detection performance and cross-modality transferability. Under all training scenarios, the YOLO-based detectors reached near-saturated accuracy levels. However, cross-domain assessments demonstrated substantial variability depending on the spectral configuration of the input imagery. Overall, the combination of UAV-based multispectral sensing with convolutional neural network detection frameworks establishes a technological basis for automated shrub monitoring and constitutes a meaningful advancement toward intelligent raspberry production systems. This integration further creates new prospects for the technological development of cultivation practices for this crop within the rapidly evolving landscape of artificial intelligence-driven agriculture. Full article

Accurate cotton boll identification and boll number estimation from UAV imagery are essential for large-scale yield prediction and precision management, yet severe leaf occlusion and complex canopy backgrounds often hinder robust performance. Here, UAV RGB images were acquired 3 days before defoliant application and at 3, 6, 9, 12, 15, and 18 days after defoliation. Cotton bolls were extracted using Mahalanobis distance, a support vector machine, and a neural network. Boll number was then estimated using an improved random forest model with multi-feature fusion. Across all defoliation stages, the NN produced the most accurate and stable boll extraction, achieving a maximum Kappa of 0.914, an overall accuracy of 95.77%, and an F1 score of 0.96. Extraction accuracy increased rapidly from 3 to 9 days after application and stabilized from 12 to 18 days. For boll number estimation, fusing the boll pixel ratio with color indices and texture features improved accuracy and consistency over time; the best performance was obtained at 18 days after application (R² = 0.7264; rRMSE = 4.9%). Overall, imagery acquired 15–18 days after defoliation provided the most reliable estimation window, supporting operational pre-harvest assessment and harvest-timing decisions. Full article

Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency. However, high vehicle mobility, time-varying channels, and limited onboard energy make task offloading and resource coordination challenging. This paper studies joint task offloading and resource allocation in a UAV-assisted IoV system, where the UAV selects its hovering position from discrete candidate sites each time slot and splits vehicular tasks between the UAV and a roadside unit (RSU) to relieve backhaul congestion and enhance edge resource utilization. Considering vehicle mobility, multi-stage queue dynamics, and UAV energy consumption for communication, computation, and movement, the online optimization of position selection, task splitting, and bandwidth allocation is formulated as a constrained Markov decision process (CMDP). The goal is to maximize the number of tasks completed within the latency deadlines while satisfying the UAV energy budget. To solve this CMDP, we propose a graph-attention-based constrained twin delayed deep deterministic policy gradient (GAT-CTD3) algorithm. A graph attention network captures spatial correlations and resource competition among active vehicles, while a Lagrangian TD3 framework enforces long-term energy constraints and improves learning stability via twin critics, delayed policy updates, and target smoothing. The simulation results demonstrate that it outperforms the comparative scheme in terms of task completion rate, delay, and energy consumption per completed task, and exhibits strong robustness in situations with dense traffic. Full article

Unmanned Aerial Vehicles (UAVs) are gradually emerging as key platforms for Integrated Sensing, Communication, and Computation (ISCC) systems in next-generation wireless networks. However, strict resource constraints and task coupling make static allocation inefficient in dynamic environments. This paper studies a UAV-driven ISCC system in which a single UAV dynamically allocates communication bandwidth, sensing resources, and computing power. Considering that sensing data in mission-critical applications is highly time-sensitive, minimizing the response time is paramount. To reduce system latency while maintaining sensing quality and energy efficiency, we propose D3PG-Light, a deployment oriented and stability-enhanced refinement of the deep reinforcement learning framework, specifically tailored for real-time resource scheduling under UAV hardware constraints. D3PG-Light incorporates an adaptive gradient stabilization mechanism, Long Short-Term Memory (LSTM), and feature fusion to enhance training stability. Simulation results based on real air–ground channel measurements show that D3PG-Light converges faster and achieves more stable learning behavior than DDPG, TD3, and the original D3PG. In particular, the proposed method reduces the 95th-percentile latency from over 100 ms to approximately 24 ms, achieves higher converged reward values, and requires fewer than 50 k model parameters. These results demonstrate the effectiveness of D3PG-Light for latency-sensitive UAV-ISCC applications. Full article

Accurate detection of oil leakage spots is essential for oilfield safety and environmental protection. However, UAV-based inspection in onshore oilfields often suffers from complex illumination conditions, such as low light, backlighting, and mixed shadows, which simultaneously degrade image visibility and obscure leakage-sensitive features, thereby causing missed detection of minute and weak-texture oil leakage targets. Unlike generic low-light enhancement or object detection tasks, the core challenge of onshore UAV oil leakage inspection lies in preserving leakage-oriented fine cues during enhancement while improving the detector’s ability to distinguish leakage targets from highly confusing oilfield backgrounds. To address this task-specific challenge, we propose a collaborative low-light enhancement and detection framework that jointly optimizes leakage-detail-preserving enhancement and multi-scale interference-suppressed detection. Specifically, an improved Retinex-based enhancement network is designed by integrating multi-scale feature aggregation, NAFNet-based denoising, and a CBAM attention mechanism to enhance brightness while preserving leakage details. The enhanced images are then fed into an improved YOLOv11 detector, where an AC-FPN module is adopted to strengthen multi-scale feature fusion and suppress background interference. Experiments on UAV oilfield datasets demonstrate that the proposed method achieves a precision of 94.25% and a mean average precision (mAP) of 87.54%, outperforming existing approaches. The proposed framework provides an effective and robust solution for oil leakage spot detection under complex illumination. Full article

Vehicle detection is a core task in smart city perception management and an important technical support for sustainable urban development and intelligent transportation optimization. In high-resolution unmanned aerial vehicle (UAV) remote sensing images, it faces challenges such as variable target scales, severe occlusion, and difficulty in modeling long-range dependencies. To address these issues, this study proposes the MCViM-YOLO algorithm, which integrates the local perception advantage of convolution with the global modeling capability of the state space model (Mamba). Based on YOLOv12, the algorithm reconstructs the neck network: it introduces the Mix-Mamba module (parallel multi-scale convolution and selective state space model) to simultaneously capture local details and global spatial dependencies, adopts the dual-factor calibration fusion module (DCFM) to adaptively fuse heterogeneous features, and employs a dual-branch attention detection head (DADH) to optimize the prediction of difficult samples (e.g., occluded, small-scale vehicles). Experiments on the VEBAI dataset demonstrate that our proposed model achieves an mAP@0.5 of 92.391% and a recall rate of 86.070%, with a computational complexity of 10.41 GFLOPs. The results show that the proposed method effectively improves the accuracy and efficiency of vehicle detection in complex remote sensing scenarios, provides technical support for traffic flow monitoring, low-carbon urban planning, and other sustainable applications, and offers an innovative paradigm for the deep integration of CNN and state space models with both theoretical research value and engineering application prospects. Full article

Unmanned aerial vehicle (UAV) communication networks are highly vulnerable to eavesdropping due to their open and dynamic air–ground channels, making physical layer security (PLS) a critical design requirement. Existing security mechanisms often struggle to adapt to large-scale UAV swarms operating under power and coordination constraints. To address this challenge, this work presents a dual-game framework that enables a group of legitimate UAVs to form optimal coalition structures through an internal coalition game, while countering coordinated eavesdropping attacks from adversarial UAVs. The framework is specifically designed for demanding real-world conditions, considering maximum power restrictions of individual UAVs and the need for secure and efficient communication with ground nodes. By jointly minimizing communication cost and maximizing swarm utility, the proposed approach enhances both security and resource efficiency. Extensive simulation results demonstrate that the proposed approach achieves up to $10\%$ improvement in secrecy rate compared with conventional frameworks, validating its effectiveness for securing large-scale UAV networks. Full article

Wetlands, frequently termed the “kidneys of the Earth,” represent one of the most vital global ecosystems. Despite their limited spatial extent, plateau wetlands function as unique ecological units that play a pivotal role in the global carbon cycle, water resource regulation, and biodiversity conservation, while exhibiting acute sensitivity to climate change. Advances in remote sensing technology—characterized by macro-scale cover-age, temporal efficiency, and non-invasive operations—have established it as a corner-stone for the dynamic monitoring and analysis of these environments. This study presents a bibliometric synthesis of 2138 publications (1982–2024) retrieved from the Web of Science Core Collection. We systematically evaluated publication trajectories, international collaborative networks, disciplinary shifts, core journals, and the spatiotemporal evolution of research hotspots. Our findings reveal an exponential growth in scholarly output alongside a marked diversification of research fields. Geographically, research is predominantly clustered around the Tibetan Plateau, flanked by the Alps and the Himalayas, with sparse representation in other regions. Future endeavors should prioritize underrepresented low-latitude and remote regions through strengthened international synergy and the integration of emerging technologies, such as UAVs and hyperspectral sensors. Full article

The intelligent detection of asphalt pavement cracks has become increasingly important for ensuring service performance of road infrastructure. Traditional manual detection has significant safety hazards and insufficient accuracy. Furthermore, existing deep learning models still face challenges, including missed detection, false alarms, and poor performance in small target detection under complex conditions. This investigation adopts unmanned aerial vehicles (UAVs) to acquire pavement distress information and develops an intelligent detection approach for asphalt pavement crack based on improved YOLOv8s. First, the Spatial Pyramid Pooling Fast (SPPF) module is replaced with the Spatial Pyramid Pooling Fast with Cross Stage Partial Connections (SPPFCSPC) module in the backbone network to enhance the multi-scale feature fusion capability. Secondly, the Convolutional Block Attention Module (CBAM) module is introduced to the neck network to optimize the feature weights in both channel and spatial attention. Meanwhile, the Efficient Intersection over Union (EIoU) loss is adopted to improve accuracy. Finally, the Crack_Dataset is established, and the ablation experiments are conducted to verify the reliability of the detection model. The research indicates that the improved model achieves Precision, Recall, and mAP@0.5 of 83.9%, 79.6%, and 83.9%, respectively, representing increases of 1.5%, 1.3%, and 1.4%, compared with the baseline model. In comparison with mainstream object detection algorithms such as YOLOv5s and YOLOv8s, the proposed method attains an F1-score, mAP@0.5, and mAP@[0.5–0.95] of 0.82, 83.9%, and 46.6%, respectively, demonstrating a performance improvement. Based on the improved detection model, a pavement crack detection system was designed and implemented using PyQt5. This system supports image, video, and real-time camera input and detection. Full article

Leaf nitrogen concentration (LNC) exhibits pronounced vertical heterogeneity across canopy layers, which affects the accuracy of nitrogen diagnosis derived from UAV-based remote sensing imagery. To address the differential contributions of leaf nitrogen from distinct canopy strata and the limitations associated with single-source features, this study proposes an integrated framework that combines cumulative LNC indicators across canopy layers with multi-source feature sets (vegetation indices and texture features). Centered on three core technical innovations—(1) incorporating canopy-layer aggregation logic into LNC modeling, (2) integrating spectral and structural information through CNN-based feature fusion, and (3) combining deep feature extraction with gradient boosting regression to improve robustness under multi-stage conditions—the framework systematically evaluates three machine learning algorithms: Random Forest (RF), a Convolutional Neural Network–Extreme Gradient Boosting hybrid model (CNN_XGBoost), and K-Nearest Neighbor (KNN) for cotton LNC estimation across multiple growth stages. The results demonstrate that cumulative canopy-layer nitrogen indicators more effectively represent overall plant nitrogen status than single-layer measurements. The integration of multi-source features further enhances model performance. Under both single-variable inputs and combined VI–TF feature sets, the CNN_XGBoost model consistently outperforms the other models in calibration accuracy and stability across all growth stages. Its optimal performance occurs during the cotton flowering and boll stage, achieving a calibration R² of 0.921. Overall, the proposed framework substantially improves the estimation accuracy of cotton LNC and provides both a theoretical foundation and technical support for precision nitrogen management and sustainable agricultural development. Full article

The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R² = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R² = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R² = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article

Space–air–ground integrated networks (SAGINs) enable flexible content delivery through satellite–UAV–ground cooperation, yet time-varying user demand and dynamic backhaul conditions pose significant challenges to efficient UAV caching. To address these challenges, this paper proposes PCICaching, a backhaul-aware and prediction-driven UAV caching framework that integrates LSTM-based popularity forecasting, cache-aware user association, and conditionally activated cooperative caching. Under normal satellite backhaul conditions, PCICaching operates in a latency-oriented mode and reduces average content delivery latency by up to 33.9% and 38.9% compared with representative GTGA-based and history-based baselines, respectively. When backhaul connectivity degrades, the proposed C3 mechanism enlarges cluster-level content coverage and maintains service continuity with only a moderate latency increase of approximately 14.2%. Moreover, the proposed sequential decomposition enables scalable online operation with per-update execution time below 100 ms. These results demonstrate that PCICaching provides a structurally adaptive and computationally efficient solution for UAV-assisted caching in SAGINs, effectively balancing latency efficiency and content availability under time-varying demand and infrastructure uncertainty. Full article

The grain aphid (Sitobion avenae) is a major pest of winter wheat, causing significant yield losses through direct feeding and as a vector of barley yellow dwarf virus (BYDV). Populations can increase rapidly under moderate temperatures and low rainfall, potentially leading to severe infestations if not effectively monitored and managed. This study develops and validates a UAV-based RGB imaging methodology, which relies on deep learning for accurate detection and assessment of Sitobion avenae in wheat crops. The RGB images are preliminarily filtered using “histogram equalization”, which allows for highlighting the infested areas. An experimental study was conducted under the specific climatic conditions of Southern Dobruja, Bulgaria, to quantify Sitobion avenae infestations. Three neural network architectures were used (DeepLabv3, U-Net, and PSPNet) in combination with three backbone models: ResNet34, ResNet50, and ResNet101. The optimal combination was determined to be the U-Net + ResNet101 model, which achieved an average F1 score of 0.982 and a Cohen’s Kappa coefficient of 0.966. The results demonstrate that UAV-based detection allows precise mapping of infested areas, enabling targeted insecticide applications and effective pest management while substantially reducing chemical inputs. These findings indicate that the proposed framework provides a reliable and scalable tool for precision pest monitoring and control in winter wheat. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article