Search Results (279)

In high-rise firefighting scenarios, unmanned aerial vehicles (UAVs) equipped with water-spraying systems are subjected to high-intensity and rapidly time-varying reaction forces induced by high-speed water jets. These forces introduce mismatched uncertainties with unknown bounds and make stable flight control particularly challenging. To address this problem, this paper proposes an adaptive robust constraint-following control (ARCFC) strategy for vector–rotor UAVs (VRUAVs). The controller is developed directly for the strongly nonlinear dynamics of the VRUAV without resorting to model linearization. Within a constraint-following-based nonlinear regulation framework, water-jet effects are explicitly modeled as rapidly time-varying uncertainties with unknown bounds, and an adaptive law is introduced to estimate conservative uncertainty bounds online for robust compensation. Lyapunov-based analysis is conducted to establish the uniform boundedness and uniform ultimate boundedness of the closed-loop system, and simulation results are presented to verify the effectiveness of the proposed approach. Compared with representative conventional control methods, the proposed ARCFC strategy provides improved disturbance-rejection capability and enhanced flight stability under demanding firefighting conditions. Full article

Unmanned aerial vehicle (UAV) path planning is one of the core technologies for realizing precision agricultural operations. In complex farmland environments involving terrain obstacles, tall tree canopies, high-voltage power lines, and restricted no-fly zones, this problem is transformed into a typical multi-objective and multi-constraint optimization problem. Dense constraints drastically narrow the feasible solution space and impose stringent requirements on the convergence, real-time performance, and robustness of planning algorithms. To address this issue, this paper proposes a novel meta-heuristic algorithm: the Agricultural Planting Whole-Cycle Management Optimization (APWMO) algorithm. By integrating the cultivation strategy aligned with crop growth cycle dynamics, the demonstration farmland-based elite guidance mechanism, and the elite archive pruning operation, it achieves a dynamic balance between global exploration and local exploitation. Comparative experiments with 15 advanced meta-heuristic algorithms on the 30-dimensional CEC2017 benchmark test suite show that APWMO achieves the best performance in terms of convergence accuracy, convergence speed, and search stability. Furthermore, the effectiveness of the proposed algorithm is verified in four 3D farmland path planning tasks with different objective weights and complexity levels. Experimental results confirm that APWMO has excellent path planning performance in complex farmland environments and can provide efficient technical support for practical agricultural UAV tasks such as plant protection spraying, crop growth monitoring, and farmland surveying. Full article

Regular inspection of defects like sprayed concrete cracking and water seepage is crucial for the long-term safety of reservoir slopes in hydraulic engineering. Traditional manual inspections suffer from low efficiency and high cost. This paper presents a weighted Traveling Salesman Problem (TSP) model established by a Genetic Algorithm (GA) to optimize Unmanned Aerial Vehicle (UAV) inspection paths for these slopes. The model integrates UAV acceleration and deceleration physics. It weights the flight distance, converting it into flight time, and uses 3D-coordinate data to form the objective function. We calibrated key parameters, including acceleration and speed thresholds, by fitting displacement-time quadratic functions to field data from a DJI Matrice 350 RTK UAV. Tests on multiple slope models show the weighted GA optimizes the planned path by 46.2%, improves average inspection efficiency by 7.90% over an algorithm simulating human decision-making, and by 7.66% over a standard (non-weighted) GA. This work provides a reference for intelligent path planning on reservoir slopes and is applicable to similar scenarios like highway and railway slopes. Full article

The operational limitations of ground-based machinery in waterlogged soils of northern regions often lead to missed agronomic treatments, resulting in substantial yield losses. This problem is important in potato production, where a delay in disease protection can quickly lead to loss of leaf mass and reduced yield. This article evaluates an integrated UAV-based approach for crop production in potato cultivation, encompassing aerial soil analysis, weed segmentation, targeted spraying, and yield prediction. A field experiment was designed using a developed GD-4 drone on a 300 m × 6 m test plot. Aerial photography was used to generate an orthophotomap for monitoring and planning pesticide applications. The UAV, operating at a 2-m altitude, achieved a 3-m spray swath, enabling complete plot coverage. Visual assessment confirmed superior plant health in the test plot compared to the control. Quantitative analysis revealed a yield of 39.06 t/ha in the test plot, a 15.2% increase over the control plot (33.91 t/ha), with a comparable percentage of marketable tubers (94.5% vs. 93.3%). The study concludes that UAV technology is a reliable means of remote sensing and offers an alternative for ensuring timely agricultural operations and enhancing yield in inaccessible terrains. Full article

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below $-20$ dB with main-beam peaks within $0.1$° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a $36\times 36$ impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 $\lambda$. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering $0$°–$60$° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article

Unmanned aerial vehicles (UAVs) are becoming an important platform for precision agriculture, supporting both high-throughput sensing and active field operations such as spraying, monitoring, and phenotyping. However, unlike general UAV applications, agricultural environments impose distinctive challenges due to heterogeneous field structures, canopy occlusion, terrain variation, dynamic disturbances, and strong coupling between navigation performance and task quality. To address this gap, this review presents a systematic analysis of UAV navigation in agricultural environments from a system-level perspective. The review first summarizes the core technical components of agricultural UAV navigation, including sensing, localization, mapping, planning, and control. It then discusses how navigation requirements vary across representative scenarios such as open fields, orchards, and terraced farmland, and examines their roles in key applications including aerial mapping, field monitoring, precision spraying, and close-range orchard operations. In addition, datasets, simulation platforms, and evaluation protocols relevant to agricultural UAV navigation are reviewed. Finally, major challenges are identified, including scene heterogeneity, perception degradation, insufficient task-semantic integration, limited control robustness, and the lack of standardized benchmarks. Future research should move toward robust, task-aware, and modular navigation architectures that support reliable and scalable agricultural UAV deployment. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Unmanned aerial vehicles (UAVs) are an alternative to traditional pesticide applications in orchards. Particularly, drones are an example of UAVs that have increased in popularity in recent years; however, relatively few studies have evaluated how spraying operation modes interact with other drone parameters within a single experimental framework. This study evaluated the effects of operation mode, application volume, flight height, and droplet size on spray coverage, droplet density, droplet spectra, and droplet size uniformity using the spraying drone DJI Agras T40 under a simulated canopy structure. A four-factorial experimental design was used; treatments included three operation modes (i.e., standard mode, fruit-tree mode, and spinning mode), two application volumes (i.e., 37.4 L/ha and 74.8 L/ha), two flight heights (i.e., 3 m and 5 m), and two droplet sizes (i.e., 150 μm and 300 μm). Operation mode was among the most influential factors affecting spray deposition quality. The spinning mode achieved the highest overall spray coverage (20.81%) and droplet density (172.44 drops/cm²), while the standard mode provided the most uniform spatial distribution. Results from the interaction analyses indicated that the parameter combination that produced the highest spray coverage within the tested ranges was an application volume of 74.8 L/ha, a flight height of 3 m, and a droplet size of 150 μm in the standard mode. For the fruit-tree mode, the highest spray coverage was observed at an application volume of 74.8 L/ha, a flight height of 5 m, and a droplet size of 300 μm. For the spinning mode, the combination associated with the highest spray coverage was 74.8 L/ha, 3 m, and 300 μm. In conclusion, the results provide data-driven guidance on how drone operational parameters influence spray deposition and can support future validation under commercial orchard conditions. Full article

Large-scale forest pest management has traditionally relied on aerial spraying; however, increasing regulatory restrictions and environmental concerns have limited its application in many regions. Unmanned Aerial Spraying System (UASS) platforms for aerial spraying have developed intensively in the last decade for pesticide application in agricultural crops but remain scarcely explored within the forestry sector. This study evaluates the feasibility of UASS-based spraying platforms for forest pest control. We tested a multi-rotor agricultural UASS in two different forest conditions: broadleaf and conifer stands. Both biological and synthetic insecticides were sprayed against two contrasting forest pests, Lymantria dispar and Adelges laricis. Defoliation and infestation intensity were used to assess treatment efficacy post-application. Results indicated differences in operational productivity between forest stand types, with higher treatment efficacy observed for L. dispar. Despite the correct dosage delivered by the UASS, the target organism showed a limited biological response in the conifer pest. In conclusion the use of UASSs in forest ecosystems is conditioned by forest-specific factors; however, these technologies show potential to be aligned with interventions targeting early-stage pest outbreaks. Full article

Unmanned aerial vehicles (UAVs) are increasingly employed in precision agronomy to support high-resolution monitoring and management of crops; however, the extent to which UAV-derived data can be translated into reliable, scalable, and decision-ready applications remains inconsistent. This review addresses this gap by critically synthesising the recent literature with a specific focus on the end-to-end data pipeline, from acquisition planning and pre-processing to data fusion, analytics readiness, and operational decision support. A systematic analysis of peer-reviewed studies published over the last five years was conducted to evaluate core agronomic applications, including crop health monitoring, precision irrigation, soil and field variability assessment, spraying, and yield prediction, with particular attention to indicators used, validation strategies, and reported agronomic outcomes. The findings indicate that monitoring and diagnostic applications are the most mature and consistently validated, whereas interventional uses and absolute yield prediction remain strongly context-dependent and constrained by operational, methodological, and regulatory factors. Across applications, pipeline robustness, uncertainty management, and reproducibility emerge as more critical determinants of agronomic value than sensor resolution alone. The review further identifies key barriers to scaling, including technical limitations, skills requirements, data integration challenges, and regulatory constraints, and outlines an innovation roadmap distinguishing currently deployable solutions from emerging developments over the next three to five years. Overall, this work provides a decision-oriented framework to support more transparent, validated, and sustainable integration of UAV technologies into modern agricultural systems. Full article

Unmanned aerial vehicles (UAVs) have revolutionized plant protection spraying due to their high efficiency and adaptability. However, the lack of rapid, portable tools for assessing droplet deposition remains a bottleneck for optimizing spray quality and improving pesticide utilization. The main purpose of this study is to develop a portable, image-based detection device with improved algorithms for real-time analysis (<3 s per card) of droplet deposition on spray cards during UAV plant protection spraying, addressing the limitations of existing methods in portability, real-time capability, and field robustness. This study presents a portable detection device integrated with advanced image processing algorithms for real-time analysis of droplet deposition on copperplate paper cards during UAV operations. The device employs a Raspberry Pi 5 as the core processor, coupled with a high-resolution camera and a standard chessboard calibration board for field-portable image acquisition. Key innovations include an adaptive background subtraction and local contrast enhancement method to address variable field lighting conditions, and an improved adhesion droplet segmentation algorithm combining iterative morphological opening operations with refined distance transform-based concave point matching. Validation on 21 field-collected cards using ImageJ as reference demonstrated a droplet extraction accuracy of 89.4%, with coverage rate improvements of 25.4% and 15.2% compared to OTSU and block thresholding methods, respectively. The adhesion segmentation relative error averaged 6.3%. This low-cost, lightweight device provides farmers and researchers with an effective tool for on-site spray quality evaluation, contributing to precision agriculture and reduced pesticide waste. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

Mid- to late-season crop protection in tall crops like corn often relies on aerial spraying, including with unmanned aerial vehicles (UAVs). However, information on UAV spray consistency remains limited. This study compared spray depositions from a DJI Agras T30 UAV, airplane, and ground sprayer on tassel-stage corn to simulate fungicide applications, while assessing the influences of key UAV operational parameters and the use of drift reducing agent (DRA). At the Alabama site, UAV applications without DRA increased spray dye concentration by 145.8% on upper leaves and 51.1% on ear leaves compared with airplane applications at 18.7 L ha⁻¹. DRA 1 reduced upper leaf deposition, but both DRAs improved ear leaf deposition relative to no DRA and airplane treatments. UAVs without DRA and airplanes showed similar variability in dye concentration, while DRA use enhanced deposition uniformity. At the Georgia site, no treatment differences were found on ear leaves, but UAV and ground sprayer treatments produced higher upper leaf deposits than airplane application. Increasing UAV swath by 1.5 m at 2.4 m height reduced deposition, while a 4.6 m swath increased it, regardless of altitude. Overall, results suggest that downwash from UAV propellers enhances spray deposition within the crop canopy, and DRAs further improve this effect and influence spray uniformity. Additional studies on UAV spray parameters and droplet size are needed to better understand downdraft influence. Full article

The rapid growth of precision agriculture has accelerated the deployment of plant protection unmanned aerial vehicles (UAVs). However, reliable data resources for vision-based intelligent supervision of operational states, such as whether a UAV is currently spraying, remain limited. Most publicly available UAV detection datasets target urban security and surveillance scenarios, where annotations emphasize object localization rather than agricultural operation state recognition, making them insufficient for farmland spraying supervision. Therefore, agricultural-oriented data resources are needed to cover diverse backgrounds and include operation state labels, thereby supporting both academic research and practical deployment. In this study, we construct and release the first multi-background dataset dedicated to agricultural UAV spraying behavior recognition. The dataset contains 9548 high-quality annotated images spanning the following six typical backgrounds: green cropland, bare farmland, orchard, woodland, mountainous terrain, and sky. For each UAV instance, we provide both a bounding box and a binary operation state label, namely spraying and flying without spraying. We further conduct systematic benchmark evaluations of mainstream object detection algorithms on this dataset. The dataset captures agriculture-specific challenges, including a high proportion of small objects, substantial scale variation, motion blur, and complex dynamic backgrounds, and can be used to assess algorithm robustness in real-world agricultural settings. Benchmark results show that YOLOv5n achieves the best overall performance, with an accuracy of 97.86% and an mAP@50 of 98.30%. This dataset provides critical data support for automated supervision of plant protection UAV spraying operations and precision agriculture monitoring platforms. Full article