Search Results (89)

Rising temperatures and droughts are triggering forest die-off in climate warming hotspots such as the Mediterranean Basin. UAVs equipped with LiDAR and multispectral sensors offer a powerful tool for surveys of tree vigor at landscape level. We used UAV-acquired LiDAR data and multispectral camera imagery to segment individual tree crowns, classify species, and assess the health status in two drought-affected forests in northeastern Spain: a mixed Pinus pinaster–Quercus ilex forest and a Pinus halepensis forest. Individual trees were segmented and classified using object-based image analysis with the Random Forest algorithm incorporating spectral, structural, and topographic variables. Greenness indices (NDVI and EVI) were analyzed in relation to crown height, topography (slope and elevation) and solar radiation, and their interactions. Analyses showed satisfactory crown segmentation (F-Score = 0.85–0.86) and species classification (Overall accuracy = 0.86–0.99), though distinguishing spectrally similar classes remained challenging. Taller P. pinaster trees exhibited higher NDVI, while taller P. halepensis displayed higher NDVI values in dense neighborhoods and on gentle slopes. These findings highlight the potential of high-resolution UAV-based remote sensing for effective near-real-time detection and attribution of forest die-off. Future research should aim to improve algorithm accuracy and better integrate field-based validation across different forest types. Full article

Land degradation in drylands increasingly threatens infrastructure and the performance of renewable energy (RE) systems through coupled hydro-chemo-mechanical changes in soil fabric, density, matric suction, and pore–water chemistry. A key gap is the limited integration of unsaturated soil mechanics with practical indicator sets used in engineering screening and operations. This narrative review synthesizes evidence from targeted searches of Scopus, Web of Science, and Google Scholar. Searches are complemented by key organizational reports and standards, as well as citation tracking. Priority is given to sources that report mechanisms linked to measurable indicators, thresholds, tests, or models relevant to dryland infrastructure. The synthesis uses the soil-water characteristic curve (SWCC) and hydraulic conductivity k(θ) to connect hydraulic state to strength and deformation and couples these with chemical indices, including electrical conductivity (EC), exchangeable sodium percentage (ESP), and sodium adsorption ratio (SAR). Practical diagnostics include the dynamic cone penetrometer (DCP) and California Bearing Ratio (CBR) tests, infiltration and crust-strength tests, monitoring with unmanned aerial vehicles (UAVs), geophysics, and in situ moisture and suction sensing. The contribution is an indicator-driven, practice-oriented framework linking mechanisms, monitoring, and mitigation for photovoltaic (PV), concentrating solar power (CSP), wind, transmission, and well-pad corridors. This framework is implemented by consistently linking unsaturated soil state (SWCC, k(θ), and matric suction) to degradation processes, measurable indicator/test sets, and trigger-based interventions across the review. Full article

This review surveys the past five years of research on energy-aware path optimization for both solar-powered and battery-only UAVs. First, the energy constraints of these two platforms are contrasted. Next, advanced computational frameworks—including model predictive control, deep reinforcement learning, and bio-inspired metaheuristics—are examined along with their hardware implementations. Recent studies show that hybrid methods combining neural networks with bio-inspired search can boost net energy efficiency by 15–25% while maintaining real-time feasibility on embedded GPUs or FPGAs. Among the remaining challenges are federated learning at the edge, multi-UAV coordination under partial observability, and field trials on ultra-long-endurance platforms like the Airbus Zephyr HAPS. Addressing these issues will accelerate the deployment of truly persistent and green aerial services. Full article

Unmanned aerial vehicles (UAVs) are increasingly utilized across civilian and defense sectors due to their versatility, efficiency, and cost-effectiveness. However, their operational endurance remains constrained by limited onboard energy storage. Recent research has focused on electric propulsion systems integrated with hybrid energy sources, particularly the combination of solar cells and advanced battery technologies to overcome this limitation. This review presents a comprehensive analysis of the latest advancements in electric propulsion architecture, solar-based power integration, and hybrid energy management strategies for UAVs. Key components, including motors, electronic speed controllers (ESCs), propellers, and energy storage systems, are examined alongside emerging technologies such as wireless charging and flexible photovoltaic (PV) materials. Power management techniques, including maximum power point tracking (MPPT) and intelligent energy control algorithms, are also discussed in the context of long-endurance missions. Challenges related to energy density, weight constraints, environmental adaptability, and component integration are highlighted, with insights into potential solutions and future directions. The findings of this review aim to guide the development of efficient, sustainable, and high-endurance UAV platforms leveraging electric-solar hybrid propulsion systems. Full article

The development of solar-powered UAVs offers major advantages, such as extended mission autonomy, marking a significant technological advance in the aerospace industry. In this context, the study demonstrated the feasibility of additive manufacturing of a solar-powered UAV by successfully completing all the steps necessary for the development of an aeronautical product. The conceptual design was the initial phase in which the needs were defined, and the basic vision of the UAV model was outlined, exploring multiple possible solutions to identify the concept capable of meeting the mission requirements (search and rescue and surveillance). The preliminary design stage included aerodynamic analysis of the aircraft and preliminary sizing of the propulsion system and solar cells. The preliminary design stage included aerodynamic analysis of the UAV model, resulting in a lift coefficient of 1.05 and a drag coefficient of 0.08 at an angle of attack of 15°. A major advantage of the design is the integration of the electrical circuit, where solar input reduced battery consumption from 92.5 W to just 40.4 W in standard operational conditions, thereby more than doubling the UAV’s autonomy (from 48 min to approximately 110 min). The detailed design stage consisted of the final design of the solar UAV model for additive manufacturing, after which the final electrical architecture of the energy system was established. The model was subsequently validated by a finite element analysis, which confirmed the strength of the wing structure by achieving a safety factor of 6.6. The use of additive manufacturing allowed the rapid and accurate production of the structural components of the UAV model, ensuring that their subsequent physical assembly would be straightforward. Full article

Traditional solar-powered unmanned aerial vehicles (SUAVs) universally adopt ultra-high aspect ratio designs to enhance aerodynamic efficiency, which unfortunately leads to significant issues such as reduced structural reliability and poor resistance to atmospheric disturbances. In contrast, SUAVs with low aspect ratios suffer from inferior aerodynamic efficiency, making it challenging to achieve long-endurance flight. This study addresses the endurance performance of low-aspect-ratio SUAVs by proposing and demonstrating a formation flight strategy to improve their cruise efficiency. To investigate the endurance characteristics of SUAVs, an energy model was established, encompassing solar cell power generation, battery energy storage, avionics, and propulsion systems. Computational fluid dynamics (CFD) simulations and surrogate modeling techniques were employed to develop a proxy model correlating formation parameters with lift and drag characteristics. Using this surrogate model, the formation parameters were optimized to minimize cruise power consumption. Energy simulations were subsequently conducted for both solo and formation flight scenarios. The results indicate that the optimized formation configuration achieved a 15% increase in maximum lift-to-drag ratio. Energy simulation results indicate that the endurance performance of SUAVs under formation flight is enhanced by 92.7%, 43.3%, and 18.8% at latitudes of 45° N, 50° N, and 60° N, respectively. These findings confirm the feasibility of using formation flight to enable sustained operation for small SUAVs. Full article

Unmanned Aerial Vehicles (UAVs) equipped with thermal and RGB cameras and enhanced by deep learning offer a powerful solution for autonomous photovoltaic (PV) system inspection. However, defect detection performance depends on flight parameters such as altitude, camera angles, speed, and solar position. This study examines the impact of various UAV flight parameters on the accurate detection of critical PV defects including hotspots, dirt from bird droppings, dust accumulation, and cell failures. For this purpose, two datasets were developed, comprising over 38,000 thermal infrared and RGB images. Using the YOLOv11 model, 21 flight configurations varying in altitude, camera tilt and pan angles, speed, and solar position were evaluated at four different times of day to assess the combined ambient and geometric effects on detection accuracy. Results indicate that low-altitude flights enhance small-object detection, while higher altitudes improve coverage at the expense of fine-detail accuracy. Dust detection is most effective when the camera aligns with the sun, whereas steep midday tilts cause reflective false positives. Thermal defect detection performs best during morning flights with moderate tilt angles. These findings emphasize the need to balance accuracy, coverage, efficiency, and safety, offering practical guidelines for effective and scalable PV inspection and maintenance. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

When a joined-wing configuration is applied to the design of solar-powered UAVs, the increasing span amplifies aeroelastic effects, while structure complexity poses greater challenges to computational effectiveness during the conceptual design phase. This paper focuses on a large-span joined-wing solar-powered UAV (LJS-UAV) engineering prototype. The structural finite element model of the whole system is constructed by developing the ‘Simplified beam-shell model’ (SBSM) and verified by a structural mode test. A numerical simulation approach is employed to comprehensively analyse and summarise the aeroelastic characteristics of the LJS-UAV from the perspectives of static aeroelasticity, flutter, and gust response. The mode test identified 30 global modes with natural frequencies below 10 Hz, indicating that the LJS-UAV possesses an exceptionally flexible structure and exhibits highly complex aeroelastic characteristics. The simulation results reveal that the structural elasticity induces significant variations in aerodynamic forces, moments, and derivatives during flight, which cannot be neglected. The longitudinal trim strategies can considerably influence the aeroelastic boundary of the LJS-UAV. Utilising the front-wing control surfaces for trim is beneficial in improving structural performance and expanding the flight envelope. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article

Water status is a key determinant of physiological performance and vineyard productivity. However, its assessment through field measurements is time-consuming and labour-intensive. Remote sensing offers a fast and reliable alternative to traditional in situ methods for the monitoring of the water status in vineyards. This study aimed to assess the potential of high-resolution multispectral imagery acquired by UAVs to estimate the vine water status. The research was conducted over two growing seasons (2021 and 2022) in a commercial Merlot vineyard in Yepes (Toledo, Central Spain), under five irrigation regimes designed to generate a range of water statuses. UAV flights were performed at two times of day (09:00 and 12:00 solar time), coinciding with in-field measurements of physiological parameters. Stem water potential (SWP), chlorophyll content, and photosynthesis data were collected. The SWP consistently showed the strongest and most stable associations with vegetation indices (VIs) and the red spectral band at 12:00. A simple linear regression model using the NDVI explained up to 58% of the SWP variability regardless of the time of day or year. Multiple linear regression models incorporating the red and NIR bands yielded even higher predictive power (R² = 0.62). Stronger correlations were observed at 12:00, especially when combining data from both years, highlighting the importance of midday measurements in capturing water stress effects. These findings demonstrate the potential of UAV-based multispectral imagery as a non-destructive and scalable tool for the monitoring of the vine water status under field conditions. Full article

Solar-powered unmanned aerial vehicles are fixed-wing aircraft designed to operate solely on solar power. Their defining feature is an advanced power system that uses solar cells to absorb sunlight during the day and convert it into electrical energy. Excess energy generated during flight can be stored in batteries, ensuring uninterrupted operation day and night. By harnessing the power of the sun, these aircraft offer key benefits such as extended flight endurance, reduced dependence on fossil fuels, and cost efficiency improvements. As a result, they have attracted considerable attention in a variety of military and civil applications, including surveillance, environmental monitoring, agriculture, communications, weather monitoring, and fire detection. This review presents selected aspects of the development and use of solar-powered aircraft. First, the general classification of unmanned aerial vehicles is presented. Then, the design process of solar-powered unmanned aerial vehicles is discussed, including issues such as the structure and materials used in solar-powered aircraft, the integration of solar cells into the wings, the selection of appropriate battery technologies, and the optimization of energy management to ensure their efficient and reliable operation. General information on the above areas is supplemented by the presentation of results discussed in the selected literature sources. Finally, the practical applications of solar-powered aircraft are discussed, with examples including surveillance, environmental monitoring, agriculture, and wildfire detection. The work is summarized via a discussion of the future research directions for the development of solar-powered aircraft. The review is intended to motivate further work focusing on the widespread use of clean, efficient, and environmentally friendly unmanned aerial vehicles for various applications. Full article

For a solar-powered unmanned aerial system (UAS), the performance and integration of the solar panel are of paramount importance. This paper examines the safety aspects of solar panels in electrical power systems, with a particular focus on the installation of solar cells onto an aircraft’s carbon fiber wing. Three distinct installation techniques are evaluated, and their respective advantages and disadvantages are discussed. A preliminary test is conducted to assess the viability of adhering commercial solar panels intended for boats using a bio-adhesive layer placed underneath the series of encapsulated solar panels. To ensure adhesion, the piece is placed under a vacuum. The subsequent test evaluates the lamination of the solar cells onto the carbon fiber skin with a resin as a component of the laminate. Finally, as a definitive solution, the adhesion of the solar panels onto the entire polymer layer used to seal the solar cells themselves was evaluated. This solution offers objective advantages in terms of adhesion, lightness and whiteness. Adhesion is guaranteed by the bond of the thermoplastic polymer used to seal the photovoltaic cells and the epoxy resin of the laminate. The bond is created through the autoclave process, which involves placing the laminate and solar cells in an oven at a specific temperature and pressure for a defined period of time. This solution results in a weight reduction of approximately three times compared to a solution not specifically designed for these materials and a reduction in thickness of approximately two times. Full article

This paper presents an analysis of the flight endurance of solar-powered unmanned aerial vehicles (UAVs). Flight endurance is usually only analyzed under the operating conditions for the location where the UAV was constructed. The fact that these conditions change in a different environment of its operation has been missed. This can be disastrous for those looking to operate such a system under different geographical conditions. This work provides critical insights into the design and operation of solar-powered UAVs for various latitudes, highlighting strategies to maximize their performance and energy efficiency. This work analyzes the endurance of small UAVs designed for practical applications such as shoreline monitoring, agricultural pest detection, and search and rescue operations. The study uses TRNSYS 18 software to employ solar radiation in the power system performance at different latitudes. The results show that flight endurance is highly dependent on solar irradiance. This study confirms that the differences between low latitudes in summer and high latitudes in winter are significant, and this parameter cannot be ignored in terms of planning the use of such vehicles. The findings emphasize the importance of optimizing the balance between UAV mass, solar energy harvesting, and endurance. While the addition of battery mass can enhance endurance, the structural reinforcements required for increased weight may impose practical limitations. The scientific contribution of this work may be useful for both future designers and stakeholders in the operation of such unmanned systems. Full article