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Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters' contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations. [ABSTRACT FROM AUTHOR]

Path planning for unmanned aerial vehicle (UAV) is the process of determining the path that travels through each location of interest within a particular area. There are numerous algorithms proposed and described in the publications to address UAV path planning problems. However, in order to handle the complex and dynamic environment with different obstacles, it is critical to utilize the proper fusion algorithms in planning the UAV path. This paper reviews some hybrid algorithms used in finding the optimal route of UAVs that developed in the last ten years as well as their advantages and disadvantages. The UAV path planning methods were classified into categories of hybrid algorithms based on traditional, heuristic, machine learning approaches. Criteria used to evaluate algorithms include execution time, total cost, energy consumption, robustness, data, computation, obstacle avoidance, and environment. The results of this study provide reference resources for researchers in finding the path for UAVs. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study's findings present optimism for smart farm management and accurate SF by improving UAV systems' response time and scalability. [ABSTRACT FROM AUTHOR]

In Brazil, agriculture consumes most of the available freshwater, especially in the Cerrado biome, where the rain cycle is marked by long periods of drought. This study, conducted at the Brazilian Agricultural Research Corporation (Embrapa) Research Corporation unit in Santo Antônio de Goiás, Goiás, Brazil, estimated evapotranspiration (ET) in different crops and soil cover. Using multispectral unmanned aerial vehicle (UAV) images, Sentinel satellite data, weather station information, and towers employing the eddy covariance method, we applied the "Simple Algorithm for Evapotranspiration Retrieving" (SAFER) to calculate ET in common bean, pasture, and semideciduous seasonal forest areas. The results showed a good agreement between UAV and satellite data, with R2 = 0.84, also validated with flow towers by the eddy covariance method. UAV-based ET was observed to correspond well to tower (EC) during full vegetative development of beans but is underestimated at the beginning of planting and in the final periods of plant senescence, due to the influence of soil or straw cover. These findings contribute to a better understanding of water dynamics in the system and to enhancing sustainable agricultural practices. This method, adapted for multispectral aerial imaging, can be applied flexibly and on-demand, in different contexts and ground cover. The study highlights the importance of integrated agricultural practices for better management of water resources and preservation of the Cerrado in balance with cultivation areas. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has received great attention as a promising technology for providing stable power to energy-constrained devices by navigating three-dimensional (3D) space, particularly in challenging environments such as maritime networks and smart cities. Additionally, UAV-enabled radar sensing has gained significant attention as a key technology for future 6G networks, as it enables high-accuracy sensing for various applications, such as target detection and tracking, surveillance, and environmental monitoring, as well as autonomous UAV operation. In this regard, we investigated UAV-enabled integrated sensing and wireless power transfer (ISWPT) systems that combine radar sensing and WPT operations on a unified hardware platform, sharing the same spectrum of resources. In order to accurately sense multiple targets and efficiently transfer power to multiple devices at the same time, we propose a method for jointly designing the transmit waveform and UAV altitude, taking into account the fundamental trade-off between radar sensing performance with the desired beam pattern and WPT performance with the desired harvested power of the devices. We first developed an effective method to obtain the optimal waveform and altitude by solving a challenging non-convex optimization problem. Based on this, we developed another efficient, low-complexity method by exploring a novel transmit waveform and optimizing its parameters to reduce computational complexity and thereby lower power consumption in UAVs. The numerical results verify that the proposed method significantly improves both radar sensing and WPT performance, as well as substantially reduces computational complexity. [ABSTRACT FROM AUTHOR]

The escalating global population and subsequent demand for agricultural products have led to a surge in agricultural waste generation, posing significant disposal challenges. Conventional disposal methods such as burning and dumping not only harm the environment but also jeopardize human health and safety. Recognizing the urgent need for sustainable waste management, researchers have increasingly focused on repurposing agricultural plant waste as a valuable resource. This paper presents a comprehensive review of the potential of agricultural plant waste in wealth creation and sustainable development. It highlights the detrimental impacts of current disposal methods and emphasizes the necessity for alternative approaches. By analyzing the physical, mechanical, and chemical properties of plant fibers, particularly cellulose, hemicellulose, and lignin, this review underscores their suitability for diverse applications. Moreover, it explores the emerging trend of utilizing pineapple leaf fiber, a sustainable and lightweight material, in structural applications, such as UAV construction. With its exceptional mechanical properties and biodegradability, pineapple leaf fiber holds promise as a viable alternative to traditional materials, contributing to a more sustainable future. In conclusion, this review advocates for a paradigm shift towards embracing agricultural plant waste as a valuable asset for economic prosperity and environmental sustainability. It underscores the importance of continued research and technological advancements to unlock the full potential of agricultural waste in fostering a circular economy and driving sustainable development globally. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the feasibility of using non-contact data collection methods—specifically, UAV (unmanned aerial vehicle)-based and terrestrial laser scanning technologies—to assess forest stand passability, which is crucial for military operations. The research was conducted in a mixed forest stand in the Březina military training area, where the position of trees and their DBHs (Diameter Breast Heights) were recorded. The study compared the effectiveness of different methods, including UAV RGB imaging, UAV-LiDAR, and handheld mobile laser scanning (HMLS), in detecting tree positions and estimating DBH. The results indicate that HMLS data provided the highest number of detected trees and the most accurate positioning relative to the reference measurements. UAV-LiDAR showed better tree detection compared to UAV RGB imaging, though both aerial methods struggled with canopy penetration in densely structured forests. The study also found significant variability in DBH estimation, especially in complex forest stands, highlighting the challenges of accurate tree detection in diverse environments. The findings suggest that while current non-contact methods show promise, further refinement and integration of data sources are necessary to improve their applicability for assessing forest passability in military or rescue contexts. [ABSTRACT FROM AUTHOR]

Sediment transported from debris‐flow initiation zones is typically stored in a topographic feature called a debris‐flow fan, the formation process of which governs secondary sediment transport further downstream. Although sediment transport from debris‐flow fans can impact sediment regimes and change landforms, the determinants of progressive fan formation are not well constrained. To identify such determinants, this study monitored debris flows and performed topographic surveys of debris‐flow fans in the Ichino‐sawa torrent (Japan) during 2016–2017. In this period, eight debris flows occurred, two of which eroded the existing fan and formed a new channel with a short recurrence interval (~40 days). Consequently, these two cases induced substantial sediment transport further downstream from the fan. The examined rainfall indices did not provide a threshold for diagnosing the occurrence of such sediment transport. Debris flows with a large flow depth and a long duration led to changes in the runout direction and subsequently formed new channels. Before these processes, the existing channel was backfilled and plugged by previous debris flows, forming a steep fan surface around the fan apex. The results suggest that increasing the magnitude and the duration of debris flows potentially triggers sediment transport from fans coupled with channel plugging. The annual sediment transport from the fan exceeded almost all sediment yields of the world rivers and was found comparable with that linked with volcanic eruptions and their aftermath. Thus, the fan‐formation process can induce substantial sediment transport, independent of volcanic perturbations and extreme climatic events, and is dependent on the sediment supply from repeated occurrence of debris flows in the initiation zones. [ABSTRACT FROM AUTHOR]

In mobile edge computing, unmanned aerial vehicles (UAVs) equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility, flexibility, rapid deployment, and terrain agnosticism. These attributes enable UAVs to reach designated areas, thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable. However, the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks. Meanwhile, there are often obstacles that affect flight safety in real UAV operation areas, and collisions between UAVs may also occur. To solve these problems, we propose an innovative A*SAC deep reinforcement learning algorithm, which seamlessly integrates the benefits of Soft Actor-Critic (SAC) and A* (A-Star) algorithms. This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function. Furthermore, our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point. This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption, thereby optimizing system-level energy efficiency. Extensive simulation results demonstrate that, compared to other algorithms, our approach achieves superior system benefits. Specifically, it exhibits an average improvement of 13.18% in terms of different computing task sizes, 25.61% higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs, and 35.78% higher on average in terms of the maximum computing frequency of different auxiliary UAVs. As for path planning, the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV, enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, unmanned aerial vehicle (UAV) formation flight has become an effective strategy for urban air mobility (UAM). However, close rotor separation during formation flight leads to complex aerodynamic interference between rotors, significantly affecting UAV flight performance and operational safety. This study systematically examines the effects of axial and lateral rotor separation on the rotor's thrust performance through wind tunnel experiments. The tests simulate horizontal, vertical, and hovering states by generating relative airflow in the wind tunnel, focusing primarily on the thrust coefficient changes of the bottom rotor at various separations. The results are compared with a single rotor operating under the same conditions without wake interference. Additionally, computational fluid dynamics (CFD) simulations using the Fluent software were conducted to investigate the effect of wake interactions by analyzing the velocity flow field between the two rotors in different separations. Both the experimental and simulation results demonstrate that rotor aerodynamic performance is notably influenced by wake interactions. Under hovering and vertical states, substantial aerodynamic interference occurs in the region directly beneath the top rotor, within 1D ≤ Z ≤ 3D. This interference gradually diminishes as the rotor separation increases. Additionally, the thrust coefficient of the bottom rotor decreases with increasing flight speed due to the wake, and at higher flight speeds, the wake tends to contract. When the lateral separation is X = 0D, the mid-sectional flow field of the two rotors exhibits symmetry; however, with lateral separation, the symmetry of the bottom rotor's wake velocity field is disrupted. During the horizontal flight, the rotor wake tilts backward due to the relative airflow, and the extent of this influence is governed by both rotor rotational speed and flight velocity. Therefore, when UAVs operate in formation, it is crucial to account for these factors affecting aerodynamic performance, and rotor separation must be optimized to enhance flight safety and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ad hoc networks offer promising applications due to their ease of use, installation, and deployment, as they do not require a centralized control entity. In these networks, nodes function as senders, receivers, and routers. One such network is the Flying Ad hoc Network (FANET), where nodes operate in three dimensions (3D) using Unmanned Aerial Vehicles (UAVs) that are remotely controlled. With the integration of the Internet of Things (IoT), these nodes form an IoT-enabled network called the Internet of UAVs (IoU). However, the airborne nodes in FANET consume high energy due to their payloads and low-power batteries. An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET. This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm (IEE-DLSA), featuring a relay mechanism to provide optimal routing with energy efficiency in FANET. The energy efficiency of IEE-DLSA is enhanced using the Red-Black (R-B) tree to ensure the fairness of advanced energy-efficient nodes. Maintaining link stability, transmission loss avoidance, delay awareness with defined threshold metrics, and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA. The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols. The evaluation metrics considered in this study include network delay, packet delivery ratio, network throughput, transmission loss, network stability, and energy consumption. [ABSTRACT FROM AUTHOR]

Fractional vegetation cover (FVC) plays a key role in ecological and environmental status assessment because it directly reflects the extent of vegetation cover and its status, yet vegetation is an important component of ecosystems. FVC estimation methods have evolved from traditional manual interpretation to advanced remote sensing technologies, such as satellite data analysis and unmanned aerial vehicle (UAV) image processing. Extraction methods based on high-resolution UAV data are being increasingly studied in the fields of ecology and remote sensing. However, research on UAV-based FVC extraction against the backdrop of the high soil reflectance in arid regions remains scarce. In this paper, based on 12 UAV visible light images in differentiated scenarios in the Ebinur Lake basin, Xinjiang, China, various methods are used for high-precision FVC estimation: Otsu's thresholding method combined with 12 Visible Vegetation Indices (abbreviated as Otsu-VVIs) (excess green index, excess red index, excess red minus green index, normalized green–red difference index, normalized green–blue difference index, red–green ratio index, color index of vegetation extraction, visible-band-modified soil-adjusted vegetation index, excess green minus red index, modified green–red vegetation index, red–green–blue vegetation index, visible-band difference vegetation index), color space method (red, green, blue, hue, saturation, value, lightness, 'a' (Green–Red component), and 'b' (Blue–Yellow component)), linear mixing model (LMM), and two machine learning algorithms (a support vector machine and a neural network). The results show that the following methods exhibit high accuracy in FVC extraction across differentiated scenarios: Otsu–CIVE, color space method ('a': Green–Red component), LMM, and SVM (Accuracy > 0.75, Precision > 0.8, kappa coefficient > 0.6). Nonetheless, higher scene complexity and image entropy reduce the applicability of precise FVC extraction methods. This study facilitates accurate, efficient extraction of vegetation information in differentiated scenarios within arid and semiarid regions, providing key technical references for FVC estimation in similar arid areas. [ABSTRACT FROM AUTHOR]

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446–0.537. The R2 of the PLSR (CIS) model is 0.323–0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model's R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status. [ABSTRACT FROM AUTHOR]

The article discusses the technology for automated neural network monitoring of the vineyard's physiological condition. Images of leaves, obtained using an unmanned aerial vehicle (UAV), are the main indicator of the physiological vineyard's condition. The proposed solution is based on the integrated use of convolutional neural network method (CNN) and machine vision technologies. To determine the optimal neural network (NN) model, a variant analysis was carried out. In accordance with its results, the YOLOv7 model was chosen, which satisfies the introduced time limit and provides the required detection quality. The training of the YOLOv7 neural network was implemented in the Python environment using the PyTorch framework and the OpenCV computer vision library. The dataset consisting of 6320 images of grape leaves (including healthy and diseased ones) has been used for neural network training. The obtained results showed that the detection accuracy is at least 91%. Visualization of monitoring results has been carried out using heatmap, allowing to obtain information about vineyard physiological condition in dynamics. The proposed mathematical model allows to calculate the monitored vineyard's area made by one complex per day. The obtained results showed that effective monitoring area using one DJI Phantom 4 UAV per day is 2.5 hectares. [ABSTRACT FROM AUTHOR]

Mechatronic systems become ever more complex because of their increasing number of interconnected safety critical components and sophistication. MBSE (Model‐based Systems Engineering) and MBSA (Model‐Based Safety Assessment) are the most commonly adopted approaches to deal with the design and safety analysis of mechatronic systems. Unfortunately, both approaches are normally adopted separately, especially in the earlier phases of system design, thus leading to a lack of communication between system engineers and the safety team. This work aims to fill that gap at a high level, that is, through process interaction. This paper proposes an enhanced V‐model for the design of safety‐critical mechatronic systems. It relates a system development process with specific safety assessment methods. Specifically, the proposed workflow details exchange flows between the RFLP (Requirements, Functional, Logical, Physical) method, the FHA (Functional Hazard Analysis), the FMEA (Failure Mode and Effects Analysis), the MBSA and simulation, and the FTA (Fault Tree Analysis). These analyses are complemented with multiphysics modeling and simulation to observe system behavior in functional and failure scenarios, with the aim of requirements verification. The design workflow has been applied to a winged Unmanned Aerial Vehicle to apply the parallel process and the necessary interaction of MBSE and MBSA approaches. The information flows between the individual activities proved effective for designing a safe system before the verification phase. The main benefit of the proposed workflow is providing both the design and safety team with some interaction points, thus avoiding a lack of safety‐critical analysis in the early phases of system design. [ABSTRACT FROM AUTHOR]

Traditional methods for quantifying the internal temperature of marine mammals require handling live animals, which is not practical for free-swimming baleen whales. Developing a less invasive, more repeatable method would significantly improve our understanding of whale health and thermal physiology. Infrared thermography (IRT) devices compatible with remotely piloted aircraft systems (RPAS) have facilitated qualitative assessments of heat signatures from marine mammals at sea, but absolute temperatures derived using this approach are rare. The goal of this study was to develop a precise empirical method for estimating intranasal temperatures of baleen whales using RPAS-based IRT. We conducted controlled field experiments and flights over North Atlantic right whales (Eubalaena glacialis, NARWs) to develop and test the methodology. Two approaches were evaluated to estimate intranasal temperatures from IRT sensor intensities: a three-point empirical line regression calibrated per flight using known-temperature objects and a generalized linear model incorporating environmental variables. Controlled field experiments demonstrated that the former approach had a median bias of −0.6°C (interquartile range: 1.5ºC), while the latter approach had unexplained negative proportional bias with increasing true temperature of the target object. After accounting for bias, the former approach yielded an average intranasal temperature of 26.9 ± 1.7°C for 21 unique NARWs. The anatomy of the mysticete upper respiratory tract and physiological heat conservation strategies may explain why estimates were low compared to internal temperatures measured from baleen whales using other techniques (30–39ºC). Variability within whales was less than ± 2°C, supporting the use of these methods to monitor the health of individuals over time. However, variability among whales was greater (up to 7ºC). Improvements in our understanding of whale physiology and respiratory mechanics and advancements in RPAS-based IRT calibrations could make this technology more reliable for assessing individual body temperatures and monitoring populations in the future. [ABSTRACT FROM AUTHOR]

Velocity obstacle method is more widely used in flight conflict detection and resolution, but it cannot give the time to start resolution, and the range of resolution is different at different time in the approaching process of two aircrafts, and the cost and efficiency of resolution will be affected by the resolution too early or too late. To solve this problem, a method of determining the maneuver time for resolution was proposed based on the velocity obstacle method. Firstly, the velocity obstacle method was used to judge whether there is conflict between unmanned aerial vehicle(UAV) and manned aircraft, adopting the heading resolution strategy, and the resolution range of heading resolution was determined. Secondly, taking the initial detection of conflicts in the surrounding area by the drone as the initial maneuver time for release, and the time corresponding to the minimum heading deviation angle for both clockwise and counterclockwise release of the drone not exceeding the maximum allowable heading adjustment amount as the latest maneuver time for release, an equation was established based on the initial and latest maneuver times to determine the maneuvering range of the drone. Finally, taking the heading adjustment range and the maneuverable range as constraints, a multi-objective resolution model was constructed with the heading resolution adjustment angle and deviation distance as the total objectives, and the Pareto optimal solution was obtained by the enumeration method to determine the heading resolution maneuver time of the UAV, and the trajectory recovery model was established, which enables the aircraft to recover the planned trajectory after completing the resolution. The feasibility and effectiveness of the proposed method in solving the problem of when the UAV starts resolution are verified by MATLAB simulation. [ABSTRACT FROM AUTHOR]

In addressing the time-consuming and computationally intensive nature of finding optimal solutions for unmanned aerial vehicle (UAV) path planning in urban logistics applications, an improved A* algorithm was proposed. Firstly, a three-dimensional environmental model was established using grid-based methods, and a multi-constrained logistics UAV path planning model was constructed based on UAV performance, transforming the path planning problem into an optimal path search problem in three-dimensional space. Secondly, the improved A* algorithm incorporates the concept of dynamic step size, Bresenham algorithm principles, and an enhanced heuristic function to enhance the quality and efficiency of the algorithm's path planning. Finally, by introducing diagonal distance, Euclidean distance, and Manhattan distance as the heuristic function for the improved A* algorithm, and combining it with the established three-dimensional environmental model, a parameter search method was used to calculate the optimal weight values, thus proposing an efficient optimization algorithm for three-dimensional path planning of urban logistics UAVs. Case studies indicate that the average time for path planning using the improved A* algorithm is reduced by at least 24. 84% compared to traditional A* and JPS algorithms, the average path length is shortened by at least 2. 89%, and the average number of search nodes is reduced by at least 82. 81% . [ABSTRACT FROM AUTHOR]

This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of estimation methods. A reverse-time Kalman estimator is proposed for this task. Accurate SOC estimations are crucial for optimizing the utilization of multiple UAVs in a collaborative manner, ensuring the efficient use of energy resources and maximizing mission success rates. Experimental results for LiFePO4 batteries are provided, showing the capabilities of the proposal for the assessment of online SOC estimators. [ABSTRACT FROM AUTHOR]

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy ( V c m a x , 0 25 ), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of V c m a x , 0 25 and GPP estimation. CNN methods achieved the best V c m a x , 0 25 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to V c m a x , 0 25 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle. [ABSTRACT FROM AUTHOR]

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. [ABSTRACT FROM AUTHOR]

In the context of global warming, rising air temperatures are increasing evapotranspiration ( ET c ) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ET c (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying ( A W D 5 , A W D 10 , and A W D 20 ) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ET c values obtained by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d−1 for the AWD and CF irrigation regimes. In addition, ET c was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d−1 for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management. [ABSTRACT FROM AUTHOR]

Forests play a critical role in the provision of ecosystem services, and understanding their compositions, especially tree species, is essential for effective ecosystem management and conservation. However, identifying tree species is challenging and time-consuming. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have emerged as a promising technology for species identification due to their relatively low cost and high spatial and temporal resolutions. Moreover, the advancement of various deep learning models makes remote sensing based species identification more a reality. However, three questions remain to be answered: first, which of the state-of-the-art models performs best for this task; second, which is the optimal season for tree species classification in a temperate forest; and third, whether a model trained in one season can be effectively transferred to another season. To address these questions, we focus on tree species classification by using five state-of-the-art deep learning models on UAV-based RGB images, and we explored the model transferability between seasons. Utilizing UAV images taken in the summer and fall, we captured 8799 crown images of eight species. We trained five models using summer and fall images and compared their performance on the same dataset. All models achieved high performances in species classification, with the best performance on summer images, with an average F1-score was 0.96. For the fall images, Vision Transformer (ViT), EfficientNetB0, and YOLOv5 achieved F1-scores greater than 0.9, outperforming both ResNet18 and DenseNet. On average, across the two seasons, ViT achieved the best accuracy. This study demonstrates the capability of deep learning models in forest inventory, particularly for tree species classification. While the choice of certain models may not significantly affect performance when using summer images, the advanced models prove to be a better choice for fall images. Given the limited transferability from one season to another, further research is required to overcome the challenge associated with transferability across seasons. [ABSTRACT FROM AUTHOR]

The atmospheric boundary layer is a crucial transitional region connecting the surface with the free atmosphere, playing a bridging role in land-sea-air interactions and the interactions between different atmospheric layers. This study utilizes rotary-wing UAVs, high-resolution lidar, and WRF simulation data to analyze the vertical distribution characteristics of temperature, humidity, wind speed, and wind direction boundary layer over the Mount Si'e region in 4–6 April 2024. The results indicate that the boundary layer temperature decreases with increasing altitude, reaching up to 18°C, while humidity decreases with height, dropping to as low as 35%. Daytime wind speeds range from 4 to 8 m/s, decreasing to 2 to 4 m/s at night. The boundary layer height can reach up to 900 m during the day and drops to 100–200 m at night, showing distinct diurnal variation characteristics. UAV observations are in good agreement with lidar and WRF simulation results, highlighting the application value of UAVs in high temporal and spatial resolution boundary layer studies. The study also reveals the significant impact of complex terrain on boundary layer characteristics, providing scientific insights into the dynamic and thermal processes of the boundary layer and offering reference value for improving regional weather forecasting and numerical simulations. [ABSTRACT FROM AUTHOR]

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture. [ABSTRACT FROM AUTHOR]

Rock avalanche disasters in alpine and gorge regions are frequent and large in scale and cause severe damage. The movement of a rock avalanche is complex and has not been fully studied. The deposits of a rock avalanche can provide valuable insights into its movement process, which is crucial in understanding the rock fragmentation mechanism and predicting disaster-affected areas. Taking the Wangjiapo rock avalanche in Yunnan Province of China as an example, the size, shape and distribution characteristics of the deposit were analyzed based on field surveys, unmanned aerial vehicle (UAV) photography and image recognition technology. Initially, 3062 deposited rock blocks were manually measured in the field. Subsequently, the Particles/Pores and Cracks Analysis System (PCAS) was employed to identify 11,357 rock blocks with an area greater than 0.1 m2 from UAV orthophotos. By comparing the characteristics of the rock blocks obtained through image recognition and manual measurement, the statistical analysis of UAV aerial imagery combined with PACS proved feasible in studying the Wangjiapo rock avalanche. The results showed that the rock block movement was accompanied by fragmentation and sorting processes; furthermore, the roundness increased with the migration distance. Small blocks were more prevalent at the foot of the slope, while irregularly shaped, large blocks dominated in source areas. The movement of huge blocks was characterized by significant potential energy-driven features and inertia advantages, allowing them to travel farther than smaller blocks, and they tended to be concentrated in the central area of the deposit. Additionally, affected by the cementation degree of breccia and the topography, the blocks in the eastern and western deposit areas exhibited different fragmentation and deposition characteristics. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV's service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem's definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency. [ABSTRACT FROM AUTHOR]

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link. [ABSTRACT FROM AUTHOR]

Object detection technology holds significant promise in unmanned aerial vehicle (UAV) applications. However, traditional methods face challenges in detecting denser, smaller, and more complex targets within UAV aerial images. To address issues such as target occlusion and dense small objects, this paper proposes a multi-scale object detection algorithm based on YOLOv5s. A novel feature extraction module, DCNCSPELAN4, which combines CSPNet and ELAN, is introduced to enhance the receptive field of feature extraction while maintaining network efficiency. Additionally, a lightweight Vision Transformer module, the CloFormer Block, is integrated to provide the network with a global receptive field. Moreover, the algorithm incorporates a three-scale feature fusion (TFE) module and a scale sequence feature fusion (SSFF) module in the neck network to effectively leverage multi-scale spatial information across different feature maps. To address dense small objects, an additional small object detection head was added to the detection layer. The original large object detection head was removed to reduce computational load. The proposed algorithm has been evaluated through ablation experiments and compared with other state-of-the-art methods on the VisDrone2019 and AU-AIR datasets. The results demonstrate that our algorithm outperforms other baseline methods in terms of both accuracy and speed. Compared to the YOLOv5s baseline model, the enhanced algorithm achieves improvements of 12.4% and 8.4% in AP50 and AP metrics, respectively, with only a marginal parameter increase of 0.3 M. These experiments validate the effectiveness of our algorithm for object detection in drone imagery. [ABSTRACT FROM AUTHOR]

Accurately counting the number of sorghum seedlings from images captured by unmanned aerial vehicles (UAV) is useful for identifying sorghum varieties with high seedling emergence rates in breeding programs. The traditional method is manual counting, which is time-consuming and laborious. Recently, UAV have been widely used for crop growth monitoring because of their low cost, and their ability to collect high-resolution images and other data non-destructively. However, estimating the number of sorghum seedlings is challenging because of the complexity of field environments. The aim of this study was to test three models for counting sorghum seedlings rapidly and automatically from red-green-blue (RGB) images captured at different flight altitudes by a UAV. The three models were a machine learning approach (Support Vector Machines, SVM) and two deep learning approaches (YOLOv5 and YOLOv8). The robustness of the models was verified using RGB images collected at different heights. The R² values of the model outputs for images captured at heights of 15 m, 30 m, and 45 m were, respectively, (SVM: 0.67, 0.57, 0.51), (YOLOv5: 0.76, 0.57, 0.56), and (YOLOv8: 0.93, 0.90, 0.71). Therefore, the YOLOv8 model was most accurate in estimating the number of sorghum seedlings. The results indicate that UAV images combined with an appropriate model can be effective for large-scale counting of sorghum seedlings. This method will be a useful tool for sorghum phenotyping. [ABSTRACT FROM AUTHOR]

The real‐time use of drone‐derived orthoimagery and ground‐penetrating radar (GPR) facilitate conjunctive ground surveying and aerial visual reference of subterranean features within cemeteries. Geospatially referenced visual outputs allow sympathetic restoration and assist in understanding historical use. Two contrasting case studies demonstrate this: The first is a subterranean wall, built to separate Catholic and Protestant burials in a civic cemetery. The second example is the accurate positioning of unmarked inhumations in an Irish Famine burial ground, which facilitated preservation as a memorial garden in a hospital complex. [ABSTRACT FROM AUTHOR]

The aim of the paper is to describe an integrated methodological approach of photogrammetric survey from UAVs (Unmanned Aerial Vehicles) that can be used in the field of engineering planning and design. In particular, a process is described in which the outputs from photogrammetric processing are implemented in different analysis or 3D modelling platforms to support all design activities in the field of engineering and in particular in the field of civil and environmental design. This approach was applied to an area of considerable landscape-environmental interest characterised by the presence of an estuary and a masonry bridge. The outputs of the photogrammetric processing have therefore constituted a valid support at cartographic level and for the production of two-dimensional CAD (Computer-Aided Design) drawings, as well as in the design of a cycle path, in the assessment of hydraulic risk or in the BIM (Building Information Modeling) design of the road infrastructure. The adopted methodology made it possible to obtain a high-performance and geometrically accurate dataset, characterised by high resolution, quickly and with low investment costs. In addition, by using UAV platforms, it was possible to reach inaccessible areas, limiting the risk of the operators specialised in surveying and optimising the time of operations, while still guaranteeing a complete product without loss of information necessary for BIM, GIS (Geographical Information System) and CAD procedures. [ABSTRACT FROM AUTHOR]

Geography has evolved from a descriptive discipline, based on exploration and mapping, to a technical and analytical science, capable of offering solutions to the complex challenges of the modern world. The transition towards a technical Geography was facilitated by the introduction of advanced statistical methods and new technologies such as GIS, remote sensing, LIDAR, UAV, etc. Autocorrelation and frequency are core concepts of the new Geography but they are common also in research from technical fields such as Engineering or Physics, which clearly proves that Geography is based on technical foundations similar to those used in other applied sciences. However, in certain states, Geography did not sufficiently adopt these tools, which led to a decrease in its relevance. In order to regain its significance, Geography must become a prospective science, capable of anticipating and manage natural and social phenomena. Universities promoting technical Geography are ready to train future leaders, while those that remain anchored in classical approaches risk becoming irrelevant. Geography’s future is therefore inextricably linked to the integration of advanced technologies and analytical methods. [ABSTRACT FROM AUTHOR]

With the advancements in fifth-generation (5G) network slicing technology and the proliferation of intelligent devices, the Internet of Things (IoT) has gained significant importance in the context of fifth-generation (B5G) networks. However, when the primary power source may become unreliable and IoT devices are vulnerable, communication becomes more critical and challenging during disasters. To increase the quality of the service, we consider unmanned aerial vehicles (UAVs) in this study as flying base stations (BS) for the emergency communication system using 5G mMTC network slicing. The suggested approach also enhances the scheduling of time slots, power, and UAV trajectory management. Initially, we optimize the UAV trajectory with various numbers of base stations. The second step is to create a non-convex fractional power allocation issue using the Dinkelbach approach. Additionally, we make a time slot distribution mechanism that increases the energy efficiency rating by evenly allocating time slots to all users. The system model is then transformed using Markov Decision Process (MDP) theory into a stochastic optimization-based problem. To solve the resource allocation problem, we provide a Dueling-Deep-Q-Networks (DDQN) based strategy that uses the Reinforcement Learning (RL) technique to maximize energy efficiency. The numerical outcomes show that the UAV-based network and the base station's efficiency have greatly improved by addressing the proposed sum-rate maximization problem by addressing trajectory optimization, power allocation and resource allocation problems. Proposed approach by efficient optimization consumes less energy of 1500 joules to 2200 joules with less resource utilization. [ABSTRACT FROM AUTHOR]

Measuring the outcome of practical interventions and actions helps to inform conservation management objectives and assess progress towards objectives and targets. Measuring success also informs future management by identifying actions that are effective and those that are not. Scrub vegetation is an important habitat type in terrestrial ecosystems, providing important shelter and food resources for biodiversity and livestock. Much of practical land management in the UK involves the monitoring and management of scrub, and current drone‐based methods of scrub collection requires expensive equipment or complex methods. A 2021 paper determined a cheap and simple way to determine scrub levels, and this could potentially be used to map temporal changes, as well as identify directional change in scrub. This study looks at whether the method outlined in the 2021 study could be used to measure temporal and directional changes in scrub cover on two nature reserves in the UK: Daneway Banks in Gloucestershire and Flat Holm Island in the Severn Estuary. Scrub levels at Daneway Banks increased from 14.63% in 2015 to 16.52% in 2017, before decreasing to 14.89% in 2021 due to managed cutting and clearing. Scrub cover at Flatholm Island decreased from 10.18% in 2019 to 8.71% in 2021. The exact locations of scrub growth and loss for each site was also calculated and mapped. This approach was found to be a viable way of measuring temporal and directional change in scrub levels. The data can also be used to reframe changes in scrub levels as a shift towards vegetation succession or reduction, to better visualise how changes in scrub levels affect overall site management goals, and is a cheaper, more accessible alternative to current methods of measuring temporal vegetation changes. [ABSTRACT FROM AUTHOR]

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively. [ABSTRACT FROM AUTHOR]

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops. [ABSTRACT FROM AUTHOR]

The temporal dynamics of canopy growth are closely related to the accumulation and distribution of plant dry matter. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have been increasingly adopted in crop growth monitoring. In this study, two potato varieties were used as materials, and treated with different combinations of nitrogen forms (nitrate and ammonium) and application rates (0, 150, and 300 kg ha−1). A canopy development model was then constructed using low-cost time-series RGB imagery acquired by UAV. The objectives of this study were to quantify the variation in canopy development parameters under different nitrogen treatments and to explore the model parameters that represent the dynamics of plant dry matter accumulation, as well as those that contribute significantly to yield. The results showed that, except for the thermal time to canopy senescence (t2), other parameters of the potato canopy development model exhibited varying degrees of variation under different nitrogen treatments. The model parameters were more sensitive to nitrogen forms, such as ammonium and nitrate, than to application rates. The integral area (At) under the canopy development curve had a direct effect on plant dry matter accumulation (path coefficient of 0.78), and the two were significantly positively correlated (Pearson correlation coefficient of 0.93). Integral area at peak flowering (AtII) was significantly correlated with yield for both single and mixed potato varieties, having the greatest effect on yield (total effect of 1.717). In conclusion, UAV-acquired time-series RGB imagery could effectively quantify the variation of potato canopy development parameters under different nitrogen treatments and monitor the dynamic changes in plant dry matter accumulation. The regulation of canopy development parameters is of great importance and practical value for optimizing nitrogen management strategies and improving yield. [ABSTRACT FROM AUTHOR]

This study applies a successful collision-avoidance method using buffered Voronoi cells (BVC) to control quadrotors. In particular, we consider the case of dealing with stationary obstacles using only the local information obtained from sensors, which has not been discussed in previous studies. In this study, we assume an unknown environment with grid-shaped obstacles. We demonstrate that four quadrotors can compute the information required for the BVC-based collision avoidance algorithm and move in the same environment without communicating with each other or receiving information from a central controller, using the local information obtained from the mounted 3D LiDAR. Simulations indicate that the system can handle static obstacles using point-cloud data obtained using 3D LiDAR as Voronoi seeds. We also demonstrate that the BVC-based collision avoidance algorithm can be applied to quadrotors controlled by a simple PD position controller without any modification to the controller. These findings show that the BVC-based collision avoidance can be easily implemented using existing commercial drones. [ABSTRACT FROM AUTHOR]

In Japan today, aging infrastructures such as dams and bridges are becoming an issue. We considered that the use of a hydrofoil multicopter would be an effective method for inspecting and surveying such facilities. When using a hydrofoil multicopter, it is possible to take off from the shore, land on the water, and dive without using a ship. The aircraft developed in this study has the capability to communicate with the land. The aircraft developed in this study is equipped with a communication buoy, and after landing on the water, a communication cable is extended from the buoy to enable underwater maneuvering via wired communication. We report on the development of a precise control system for flight and diving of this aircraft. [ABSTRACT FROM AUTHOR]

This paper proposes a method for generating highly accurate point cloud maps of orchards using an unmanned aerial vehicle (UAV) equipped with light detection and ranging (LiDAR). The point cloud captured by the UAV-LiDAR was converted to a geographic coordinate system using a global navigation satellite system / inertial measurement unit (GNSS/IMU). The converted point cloud is then aligned with the simultaneous localization and mapping (SLAM) technique. As a result, a 3D model of an orchard is generated in a low-cost and easy-to-use manner for pesticide application with precision. The method of direct point cloud alignment with real-time kinematic-global navigation satellite system (RTK-GNSS) had a root mean square error (RMSE) of 42 cm between the predicted and true crop height values, primarily due to the effects of GNSS multipath and vibration of automated vehicles. Contrastingly, our method demonstrated better results, with RMSE of 5.43 cm and 2.14 cm in the vertical and horizontal axes, respectively. The proposed method for predicting crop location successfully achieved the required accuracy of less than 1 m with errors not exceeding 30 cm in the geographic coordinate system. [ABSTRACT FROM AUTHOR]

Urban planning and comprehensive understanding of human-environmental interactions within cities require precise and up-to-date information of urban impervious surfaces. In this regards, the emergence of accurate data, particularly short-range photogrammetric data with high spatial and spectral resolutions has proven to be essential. Urban impervious surfaces are diverse and comopsed of disperate materials; this introduces challenges in the extraction of these surface from remote sensing images. Despite this diversity, only limited types of impervious surfaces (up to six classes) can be diffierenciated in remote sensing approaches, that is insufficient to meet the requirements of researchers, managers, and urban planners. Therefore, this study proposes a novel classification scheme to extract as much information as possible from UAVs visible and elevation data. The substantial influence of classification scheme on segmentation accuracy was assessed. To achieve this objective, a classification scheme consisting of 17 classes was developed to identify the various types of impervious surfaces present in Ahvaz (with diverse impervious surfaces), Iran. Then, UAV RGB and digital elevation model (DEM) images with 5 and 10 cm spatial resolutions, respectively, were segmented through 45 different parameter combinations in the study area. Then, the optimal parameter for segmention the urban impervious surface classes individually and collectively were selected. Finally the images were classified by two methods, namely simple and stratified, using the SVM algorithm. Results demonstrated that impervious surfaces could be accurately extracted in 16 distinct classes with error less than 20%. Image segmentation and classification based on the stratified method in comparison to simple ones improved the urban impervious surface extraction overall accuracy from 72 to 81%, while also improving the minimum user and producer accuracies from 53 and 14 percent to 55 and 62 percent, respectively. The proposed detailed classification scheme can improve urban impervious surface classification in diverse and heterogenous urban environment. [ABSTRACT FROM AUTHOR]

To address the difficulties in manual counting for the uneven distribution and severe occlusion of watermelons in natural environments, this study utilizes drones and smartphones to collect videos and images, combined with manual annotation to establish a dataset for Sanbai melons and Ningxia selenium sand melons. A watermelon video automatic counting method based on the YOLOv7-GCSF model and an improved DeepSORT algorithm is proposed. The lightweight YOLOv7 model with GhostConv is enhanced with GBS modules, G-ELAN modules, and G-SPPCSPC modules to increase the model’s detection speed. Some ELAN modules are replaced with the C2f module from YOLOv8 to reduce redundant information. The SimAM attention mechanism is introduced into the MP module of the feature fusion layer to construct the MP-SimAM module, which is used to enhance the model's feature extraction capability. The CIoU loss function is replaced with the fasterconverging, lower-loss Focal EIoU loss function to increase the model's convergence speed. In video tracking and counting, a mask collision line mechanism is proposed for more accurate counting of Sanbai melons and Ningxia selenium sand melons. The results show that in terms of object detection: the four improvements to the YOLOv7-GCSF model have all enhanced the model’s performance to some extent. Specifically, compared to the YOLOv7 model, the construction of the MP-SimAM module increased accuracy by 1.5 percentage points, indicating a greater focus on Sanbai melons and Ningxia selenium sand melons. The addition of GhostConv reduced the model size by 28.1MB, demonstrating that the construction of GBS, G-ELAN, and G-SPPCSPC modules effectively reduced the model size and improved detection speed. The incorporation of the C2f module reduced the model's floating-point operations (FLOPs) by 77.5 billion, indicating that the model has eliminated most of the redundant information. The addition of the Focal EIoU loss function significantly increased the model’s convergence speed, indicating further enhancement of the model's learning ability. The improved YOLOv7-GCSF model achieved an accuracy (P) of 94.2% and a mean average precision (mAP0.5) of 98.2%, which is 5.0, 2.3, 21.9, and 14.9 percentage points higher in accuracy and 3.7, 0.3, 4.6, and 9.3 percentage points higher in mean average precision compared to YOLOv5, YOLOv7, Faster RCNN, and SSD, respectively. In terms of model lightweighting, the YOLOv7-GCSF model has seen a decrease of 1.18M and 0.11M in the number of parameters compared to the YOLOv4-Ghostnet and YOLOv7-Slimneck models, respectively. Compared to the original YOLOv7, the YOLOv7-GCSF model has reduced the parameter count and model size by 0.57M and 18.88MB, respectively. In terms of object tracking: the improved DeepSORT multi-object tracking accuracy is 91.2%, and the multi-object tracking precision is 89.6%, which is 5.0 and 13.7 percentage points higher in tracking accuracy and 3.7 and 13.1 percentage points higher in tracking precision compared to Tracktor and SORT, respectively. Comparing the improved model with manual counting results, the determination coefficient for the counting results of Sanbai melons and Ningxia selenium sand melons is 0.93, the average counting accuracy is 96.3%, and the average absolute error is 0.77, indicating that the error between the improved model and manual counting is small. This approach, by enabling effective counting of watermelons in agricultural fields, provides a technical methodology for the forecasting of watermelon yields. [ABSTRACT FROM AUTHOR]

Coconut coir-based porous carbon (CCPC) and magnetic iron (Fe) particle composite (CCPC/Fe) have been developed and characterized for potential applications in unmanned aerial vehicles (UAVs). Coconut coir was extracted from coconut husks and Fe compounds are impregnated to form CCPC/Fe. A comprehensive analysis was conducted, encompassing XRD analysis, revealing the presence of crystalline Fe particles and amorphous carbon, with broadened peaks and a crystalline size of 16.68 nm. Raman spectroscopy showed distinctive structural properties. UV-Vis spectroscopy exposed unique absorption peaks and SEM imaging confirmed the retention of porous structures and the successful integration of Fe particles. EDS analysis provided insights into the elemental composition, emphasizing the varying compositions of the sample. The VNA testing reflection loss value of -26 dB (2 mm thick sample) calculated from VNA testing indicated EM wave attenuation of > 99 %. These results underscores the potential of CCPC/Fe, making it an attractive candidate for advanced applications like EM wave attenuation in UAVs. The research contributes to materials science by offering a sustainable, cost-effective solution to enhance electromagnetic compatibility in UAVs, ensuring dependable and secure operation across diverse environments. [ABSTRACT FROM AUTHOR]

Nonlinear control theory applied to unmanned aeronautical vehicles is an engineering topic that has received higher and higher popularity during the last decade. Model-based control approaches have shown increased performance in flight control accuracy and robustness compared to model-free proposals based on parameter adaptation and estimation. However, model-based structures need more computational efforts in terms of spatial and temporal variables. To avoid these constraints, the latest drone flight controls are based on quaternion models, ensuring more advanced computational performances. To this aim, this paper deals with a flight control algorithm of a quadrotor, in which the mathematics model of the plant is defined in terms of quaternions. Additionally, when aerial vehicles are used in specific applications such as slung load transportation and agriculture fields, among others, the variation of the mass receives high importance since it could make the entire system unstable. In the same line of ideas, this paper presents a H ∞ strategy, combined with a Super-Twisting Sliding-Mode Control, ensuring the control objective of the mass variations identification, and trajectory tracking, to be solved. The stability analysis of the proposed control approach is also discussed, and the quality and performances of the presented control strategy are tested by simulations, in an interesting case in which mass variations and external perturbations cannot be negligible. [ABSTRACT FROM AUTHOR]

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties. [ABSTRACT FROM AUTHOR]