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Unmanned aerial vehicles (UAV) are widely used in literature for object detection utilizing convolutional neural networks (CNN). However, most UAVs make use of GNSS sensors for localization, which have low reception in indoor situations. Therefore, this study aimed to investigate the implementation of a multi-UAV object detection system and navigation with the aid of particle swarm optimization (PSO) in ultra-wideband (UWB) positioning systems for GNSSdenied environments, such as inside factories and warehouses. The performance of UWB systems was investigated to determine its viability in the PSO model. An object detection system based on the YOLOv5 network was trained with custom training images and subsequently evaluated with test images. The results of the object detection network were fed as inputs into PSO algorithms. Furthermore, different PSO algorithms were evaluated to determine the suitability for multi-UAV navigation and object detection. The results showed that UWB systems had sufficient accuracy for indoor localization, object detection, and navigation applications. YOLOv5 detection model detected objects with an F1 score of 0.93, given the optimal threshold of 0.8. Regarding the evaluation of PSO algorithms, the stochastic inertia weight variant of PSO algorithms (Sto-IW PSO) performed effectively across all metrics considered in the study compared to other algorithms that only performed effectively in one. Recommendations included the actual implementation of the system with multiple UAVs through field experiments and further refinements to PSO algorithms in order to match the kinematics and response time of the UAVs. [ABSTRACT FROM AUTHOR]

For the three-dimensional path tracking control problem of fixed-wing unmanned aerial vehicles, an inner and outer loop controller based on globally stable integral sliding mode S-plane model is proposed in this paper. The outer loop is controlled by the globally stable integral sliding mode, and the inner loop is controlled by the S-plane. Firstly, the globally stable integral sliding control law is designed for the outer loop, and the stability of the control law is proved using the Lyapunov theory. Then the S-plane controller is designed for the instruction signal of the inner loop. Due to the complexity of derivation in the S-plane controller, a second-order differentiator is introduced. The simulation results show that the proposed controller can track the ideal path accurately, which has good control performance and anti-interference performance. [ABSTRACT FROM AUTHOR]

Weed detection plays a critical role in smart and precise agriculture systems by enabling targeted weed management and reducing environmental impact. Unmanned aerial vehicles (UAVs) and their associated imagery have emerged as powerful tools for weed detection. Traditional and deep learning methods have been explored for weed detection, with deep learning methods being favored due to their ability to handle complex patterns. However, accuracy rate and computation cost challenges persist in deep learning-based weed detection methods. To address this, we propose a method on the basis of the YOLOv5 algorithm to deal with high accuracy demand and low computation cost requirements. The approach involves model generation using a custom dataset and training, validation, and testing sets. Experimental results and performance evaluation validate the proposed method that indicates the research contributes to advancing weed detection in smart and precise agriculture systems, leveraging deep learning techniques for enhanced accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications. [ABSTRACT FROM AUTHOR]

Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAVassisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms. [ABSTRACT FROM AUTHOR]

Summary: In the rapidly developing field of unmanned aerial vehicle (UVA) technology, solving local optimal problems and achieving efficient smooth planning are crucial for improving the operational efficiency and safety of UVA systems. To address these needs, our study introduces a novel optimization algorithm, called IWPA‐APF, which aims to improve path planning efficiency. This algorithm is a fusion of the artificial potential field (APF) method and the improved wolf‐pack algorithm (IWPA) to solve common problems such as local optima and inefficient planning in the path planning process. The IWPA‐APF algorithm improves search efficiency and accuracy by incorporating an adaptive step size that significantly refines the key behaviors of the traditional IWPA, which is based on the wolf pack algorithm (WPA). In addition, the algorithm incorporates specific planning constraints, such as turn angles and tolerance limits, to minimize getting stuck in local optima. The process involves generating initial plans through multiple iterations of the IWPA, followed by further refinement using the method of integrating APF's repulsive force field. Simulation results show that the IWPA‐APF algorithm outperforms traditional methods, offering shorter flight distances and improved safety, thereby establishing itself as a robust solution for UAV path planning in obstacle‐rich environments. [ABSTRACT FROM AUTHOR]

Timely identification of a fire's origin in its initial stages is essential for minimizing human as well as material losses. Hence, formulating a methodology and algorithm for autonomously planning the flight path of an unmanned aerial vehicle (UAV) during fire monitoring to identify ignition sources early is a pressing objective. The proposed ignition detection method comprises three flight plans (FPs); Flight Plan A (FPA) involves surveying the monitored area with tacks, and assessing harmful substance concentrations in each pixel. Upon identifying a pixel surpassing the threshold concentration, Flight Plan B (FPB) directs the UAV control for targeted navigation, employing local planning based on calculating local differential operators within a nine-element mask. FPB enables the UAV to directly reach and pinpoint the fire source coordinates. Flight Plan C (FPC) outlines the UAV's return journey to the departure point from any pixel within the monitoring zone. A multi-criteria algorithm for UAV flight path control has been devised, facilitating the determination of local target pixels and subsequent Local Flight Plan (LFP) construction. The guiding principle for constructing an LFP is the 'at least three pixels on the tack' rule, yielding a nine-element matrix with known harmful substance concentrations in the target pixel. It enables the identification of pixels within the LFP where obtaining this matrix is unattainable. Mathematical modelling of the UAV flight control algorithm, as per the proposed methodology, was executed using MATLAB® R2019b, demonstrating control stability and accelerated attainment of ignition source pixel coordinates. The achieved speed surpassed the set goal by 1.5 to 2 times, contingent on the ignition source's location concerning the monitored territory's flyby direction. [ABSTRACT FROM AUTHOR]

Classifying grapevine varieties is crucial in precision viticulture, as it allows for accurate estimation of vineyard row growth for different varieties and ensures authenticity in the wine industry. This task can be performed with time-consuming destructive methods, including data collection and analysis in the laboratory. In contrast, unmanned aerial vehicles (UAVs) offer a markedly more efficient and less restrictive method for gathering hyperspectral data, even though they may yield data with higher levels of noise. Therefore, the first task is the processing of these data to correct and downsample large amounts of data. In addition, the hyperspectral signatures of grape varieties are very similar. In this study, we propose the use of a convolutional neural network (CNN) to classify seventeen different varieties of red and white grape cultivars. Instead of classifying individual samples, our approach involves processing samples alongside their surrounding neighborhood for enhanced accuracy. The extraction of spatial and spectral features is addressed with (1) a spatial attention layer and (2) inception blocks. The pipeline goes from data preparation to dataset elaboration, finishing with the training phase. The fitted model is evaluated in terms of response time, accuracy and data separability and is compared with other state-of-the-art CNNs for classifying hyperspectral data. Our network was proven to be much more lightweight by using a limited number of input bands (40) and a reduced number of trainable weights (560 k parameters). Hence, it reduced training time (1 h on average) over the collected hyperspectral dataset. In contrast, other state-of-the-art research requires large networks with several million parameters that require hours to be trained. Despite this, the evaluated metrics showed much better results for our network (approximately 99% overall accuracy), in comparison with previous works barely achieving 81% OA over UAV imagery. This notable OA was similarly observed over satellite data. These results demonstrate the efficiency and robustness of our proposed method across different hyperspectral data sources. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV–satellite data fusion have emerged as two prominent approaches for the estimation of vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for the mapping of crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV–satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV–satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV–RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root mean square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian process regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is observed for the UAV–Landsat and UAV–PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, the maximum performance is attained with the Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for the PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, the RapidEye data yield the most stable performance, with the R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data, which enables the effective monitoring of canopy chlorophyll in small agricultural lands. [ABSTRACT FROM AUTHOR]

Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false alarm rates. For SAR tasks in noisy environments, it is more important to find tiny objects than localize them. Hence, considering background clutter and strong glint noise, in this study, a noise suppression methodology for maritime scenarios (HDetect-VS) is established to achieve tiny human object enhancement and detection based on visual saliency. To this end, the pixel intensity value distributions, color characteristics, and spatial distributions are thoroughly analyzed to separate objects from background and glint noise. Using unmanned aerial vehicles (UAVs), visible images with rich details, rather than infrared images, are applied to detect tiny objects in noisy environments. In this study, a grayscale model mapped from the HSV model (HSV-gray) is used to suppress glint noise based on color characteristic analysis, and large-scale Gaussian Convolution is utilized to obtain the pixel intensity surface and suppress background noise based on pixel intensity value distributions. Moreover, based on a thorough analysis of the spatial distribution of objects and noise, two-step clustering is employed to separate objects from noise in a salient point map. Experiments are conducted on the SeaDronesSee dataset; the results illustrate that HDetect-VS has more robust and effective performance in tiny object detection in noisy environments than other pixel-level algorithms. In particular, the performance of existing deep learning-based object detection algorithms can be significantly improved by taking the results of HDetect-VS as input. [ABSTRACT FROM AUTHOR]

Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions. [ABSTRACT FROM AUTHOR]

Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia's weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes. [ABSTRACT FROM AUTHOR]

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover. [ABSTRACT FROM AUTHOR]

The self-trajectory determination is an effective method to continuously track the target's motion position. However, the traditional methods are relied on auxiliary parameters, which cause the problems of information loss and error accumulation. In order to handle these problems, we propose a direct self-trajectory determination algorithm based on evolutionary particle filter (EPF) for unmanned aerial vehicle (UAV) mounted with an antenna array. Firstly, the array sensing data are eigenvalue decomposed to obtain the observation function and the state transition function is constructed with the process control parameters. Then, particles are distributed randomly around the position of UAV and their weighted values are estimated using the likelihood function derived from the observation function. The resampling algorithm is adopted to select particles with larger values and the position of UAV is determined from these reserved particles. To overcome the decrease in particle diversity, the reserved particles get more dense after mutation and the new particle group for next moment is obtained with the state transition function. In this way, the self-trajectory is iteratively refined with EPF. Finally, the simulation test and the practical experiment based on UAV are conducted to verify that the proposed algorithm is more accurate and more stable when tracking real-time positions of UAV. [ABSTRACT FROM AUTHOR]

The fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat. In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m2 m−2 for GAI and of 0.80–1.21 g m−2 for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications. [ABSTRACT FROM AUTHOR]

In the next generation of smart cities, Unmanned Aerial Vehicles (UAV) also known as drones are playing a vital role in many advanced applications such as power transmission line inspection, transportation, aerospace and surveillance etc. Due to the excessively high and wide transmission tower heights, the conventional methods of power line inspection are generally ineffective. This manuscript’s primary focus is the development of an autonomous UAV/ quadcopter that can hover over transmission towers and capture photographs and videos by flying along pre-planned routes. Quadcopters have a distinct feature that distinguishes them with the existing aerial vehicles and have a vital role in wide range of applications such as live monitoring of traffic and crowded areas, remote locations, delivery and inspection. This manuscript also explains about the advanced sensors & components such as Global Navigation Satellite System (GNSS), optical flow sensor and Here Link etc. required for fabrication of an autonomous quadcopter for power transmission line applications. The fabricated quadcopter includes a light weight S-500 frame equipped with intelligent controller such as Pixhawk cube orange (2.1) and NVIDIA nano board for receiving and analyzing the data from the onboard sensors and camera based on pre-determined criteria. The proposed approach increases effectiveness and accuracy, has a promising future for intelligent insulator detection and inspection which is a valuable addition to power networks. The suggested deep learning technique has a detection speed of 51.8 frames/sec and a detection accuracy of up to 90.31 percent. The suggested DL algorithm has a promising future in terms of intelligent insulator inspection in power grids. [ABSTRACT FROM AUTHOR]

A new enhanced multi-strategy bottlenose dolphin optimizer is proposed. • The superiority of the proposed algorithm is verified by comparing with efficient intelligent algorithms. • A three-dimensional path planning model for unmanned aerial vehicles with time, threat, height and smooth cost is studied. • The proposed algorithm is applied to solve the three-dimensional path planning problem for unmanned aerial vehicles. • The quality of the proposed algorithm based path planning results is better than other approaches. The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints. [ABSTRACT FROM AUTHOR]

As one of the worst ecological disasters in the world's oceans, red tide seriously threatens marine ecology. Red tide monitoring is essential for preserving the ocean ecosystem. However, traditional photogrammetric processing workflows, e.g. SfM (Structure from Motion) and MVS (Multi-view Stereo) based image orientation and dense matching, do not apply to offshore images due to the low texture of ocean color. The primary contribution of this study is a UAV (Unmanned Aerial Vehicle)-based photogrammetric solution for red tide monitoring and direct geo-localization. First, a direct geo-localization model has been created, which solely uses onboard sensor data for the 3D coordinate calculation of 2D image targets based on the collinear equation. Second, two UAVs are chosen as photogrammetric systems that can supply the necessary data for the direct geo-localization model, including high-resolution images, camera intrinsic and extrinsic parameters. Third, a human annotation technique has been utilized to identify red tide regions whose ground polygons can be further estimated using the direct geo-localization model, taking into account how difficult it is to detect red tides automatically. Finally, the accuracy of direct geo-localization and the viability of the proposed solution has been evaluated and verified using real UAV datasets. The experimental results show that the direct geo-localization precision is better than 20 m when only onboard sensor data is used. The suggested workflow is appropriate for monitoring red tides, which can provide critical information for managing marine ecosystems. The executable toolkit would be made publicly available at https://github.com/json87/PhotoDigitizer. [ABSTRACT FROM AUTHOR]

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with locationspecific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and rededge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) is a critical criterion for assessing the ecological value and productivity of grasslands. Quickly and accurately estimating the AGB of grasslands has become a matter of great concern in grassland ecosystem research. By combining ground ASD hyperspectral data and airborne hyperspectral data, this study successfully extracted the sensitive bands of canopy reflectance and calculated the various vegetation indexes, thus providing information of more dimensions for above-ground biomass estimation. Biomass inversion-based yield modeling results reveal that the 377 nm raw band reflectance of the ASD data and the narrow-band vegetation index of the Resonon data exhibit outstanding performance in yield modeling. Comparative analysis indicates that the Resonon hyperspectral data demonstrate a distinct advantage in assessing meadow steppe yield, and the quadratic polynomial model based on its narrow-band SAVI achieves an R² of 0.553 and an RMSE of 37.53. [ABSTRACT FROM AUTHOR]

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In this research, we proposed multi-criteria decision-making (MCDM) model using CRITERIA (Criteria Importance through Intercriteria Correlation) and MARICA (Multi-Attribute Rating Analysis with Ideal Concepts) methods integrated with neutrosophic logic, which is considered a powerful tool in dealing with uncertainty and ambiguity. The CRITIC method calculates the weight of criteria, whereas it takes into account the correlations and relationships between the criteria, whether they are positive or negative, unlike other methods that consider the criteria separately, which allows for a more accurate and comprehensive analysis of the decision problem. The MARICA method is used also to rank the alternatives. It allows decision-makers to evaluate alternatives according to how well they perform across multiple criteria by considering several factors at once. This helps increase the effectiveness of judgments by taking into account all relevant factors. Moreover, MARICA is a user-friendly method that doesn't require complex mathematical calculations, making it accessible to anyone who wants to make sound choices. The UAV with the highest ranking is the one that will be chosen and represents the best among the alternatives. The proposed model proved its effectiveness by applying it to an experimental case. [ABSTRACT FROM AUTHOR]

Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Chl) content at different growth stages. The study used a fully randomized experimental design with four treatments of nitrogen fertilizer (0, 50%, 100%, and 150%). Ten plants were randomly selected in each plot at the phenological stages of 8 leaves (V8) and tasseling growth stages (VT) for sampling. Leaf samples were taken to measure total nitrogen (N) and chlorophyll (Chl) content. Mathematical models were created using vegetation indices extracted from aerial multispectral images to estimate the amount of nitrogen and chlorophyll. The models were evaluated using the leave-one-out cross-validation method. The results showed that there is a significant positive relationship between the leaf dry weight (LDW), the Chl and N content with the amount of nitrogen fertilizer used. So, the results indicated that the REIP index is suitable for estimating chlorophyll content in both the V8 (R² of 0.997) and VT (R² of 0.980) growth stages. Additionally, the REIP index was found to be an appropriate index for estimating N content in both growth stages (R² of 0.980). It can be concluded that aerial multispectral remote sensing technology is a reliable method for estimating corn nitrogen and chlorophyll content. [ABSTRACT FROM AUTHOR]

High-quality and efficient rice production via unmanned construction has been required in modern agriculture. Rice unmanned production can also be accelerated due to the shortage of labor supply at present. In particular, unmanned aerial seeding (UAS) has attracted extensive attention, due to its free of terrain, operating cost-saving, and high efficiency. However, the multiple cropping strategy has been practiced to confine the aerial seed emergence and growth in many rice regions, leading to compression of the rice growth period and a large amount of straw return to the field. This study aims to guarantee the stable and high yield of UAS. The research object was taken as the locally suitable varieties in the early stage. A series of experiments were conducted in the rice-wheat double-maturing region of Jiangsu Province, China. Firstly, rice production was compared between UAS and unmanned carpet-transplanting under straw-return conditions. Subsequently, three seedling treatments were set as 150×104, 195×104 , and 240×104 plants/hm² in UAS. The yield and quality were then evaluated among different population densities. The high-yield cultivation and techniques of UAS rice were summarized on the ecological, growth, and development. The UAS was initially identified with the following ecological characteristics: 1) There was a shortened growth period at low temperatures and light resources; 2) A large amount of straw returned to the field was caused by a low quality of tilled land, resulting in a seedling growth adversity; 3) The economic and ecological costs were reduced with the global warming potential to sustainable development. Besides, the growth and developmental features were obtained: 1) The individual growth was reduced to deteriorate the plant configuration, indicating the ever-increasing lodging risk; 2) There were main stems and tillers forming panicles, while a large wave in the population tiller number; 3) The yield level was restricted to a forward growth center and insufficient accumulation of population dry matter after spiking; 4) There was a deterioration in the rice processing, appearance, and nutritive qualities, whereas, the tasting qualities were improved. Some suggestions were given for the high-yield cultivation approaches. The optimal seedling density was achieved in 195×104 plants/hm² for the UAS conventional japonica rice with both high yield and high quality under straw return. Moreover, the basic seedling and the proportion of main stem panicles were appropriately increasing under the panicle formation, considering both main stems and tillers suitable for cultivation management in favor of the stable and abundant yield of UAS conventional japonica rice. The key technologies of unmanned aerial sowing were also explored for the conventional japonica rice under the straw returning to the field, in terms of variety selection, seed processing, sowing period, tillage and furrow system, planting density, fertilizer and irrigation management, and plant protection. And the prospect for the future application of UAS technology was also proposed: 1) Drone technology can be realized to develop the long duration, lightweight models and new types of seeders; 2) Industry standards can be established to generalize the production of drone accessories; 3) Evaluation can be performed to optimize the distribution of UAS technology, according to regional adaptability. Adequate research can also be conducted on the high-yield and high-quality cultivation approaches. The finding can provide theoretical support for the large-scale application of UAS rice. [ABSTRACT FROM AUTHOR]

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Despite the great development and advancement of technology over time, the problem of disaster and crisis management and dealing with it remains a major and great challenge. Early detection of natural disasters, strict laws against man-made disasters, and even the enforcement of the safety requirements for industrial disasters could not stop the occurrence of disasters and crises that leave devastation, general disability, suffering, and deprivation, in addition to injuries, wounded, victims, and even missing and dead human beings. Therefore, technologies, algorithms, and modern methods such as mechanical, electronic, robots, image, and signal processing, artificial intelligence, wireless communication, and so on must be harnessed to deal with disasters after their occurrence as well as limit their effects. Because preserving the lives of people and helping them is greatly important, this research has been prepared to review the work and techniques of researchers. The reviewed research dealt with the early detection of disasters and managing them in the fastest time and with high efficiency, including detecting and locating victims and also relieving survivors to reduce the psychological, physical, and economic impact of these disasters. Also, the paper presented the development of using some technology as a robot in this field. This paper can be a base for other researchers and rescue workers to improve and enhance their operations or mission of managing disasters or crises. [ABSTRACT FROM AUTHOR]

The paper presents a security control scheme for unmanned aerial vehicles (UAVs) against desired trajectory attacks. The key components of the proposed scheme are the attack detector, attack estimator, and integral sliding mode security controller (ISMSC). We focus on malicious tampering of the desired trajectory sent by the ground control station (GCS) to the UAV by attackers. Firstly, we model attacks by analyzing the characteristics of desired trajectory attacks. Secondly, an integrated attack detection scheme based on an unknown input observer (UIO) and an interval observer is presented. Subsequently, a robust adaptive observer (RAO) is employed to compensate for the impact of attacks on the control system. Thirdly, an ISMSC with an attack compensation mechanism is established. Finally, simulation results are provided to verify the effectiveness of the proposed scheme. The proposed detection scheme can not only detect desired trajectory attacks but also distinguish them from abrupt unknown disturbances (AUDs). By utilizing ISMSC method, UAVs under desired trajectory attacks can fly safely. The proposed comprehensive framework of detection, estimation and compensation provides a theoretical basis for ensuring cyber security in UAVs. [ABSTRACT FROM AUTHOR]

Condition monitoring of power transmission lines is an essential aspect of improving transmission efficiency and ensuring an uninterrupted power supply. Wherein, efficient inspection methods play a critical role for carrying out regular inspections with less effort & cost, minimum labour engagement and ease of execution in any geographical & environmental conditions. Earlier various methods such as manual inspection, roll-on wire robotic inspection and helicopter-based inspection are preferably utilized. In the present days, Unmanned Aerial System (UAS) based inspection techniques are gradually increasing its suitability in terms of working speed, flexibility to program for difficult circumstances, accuracy in data collection and cost minimization. This paper reports a state-of-the-art study on the inspection of power transmission line systems and various methods utilized therein, along with their merits and demerits, which are explained and compared. Furthermore, a review was also carried out for the existing visual inspection systems utilized for power line inspection. In addition to that, blockchain utilities for power transmission line inspection are discussed, which illustrates next-generation data management possibilities, automating an effective inspection and providing solutions for the current challenges. Overall, the review demonstrates a concept for synergic integration of deep learning, navigation control concepts and the utilization of advanced sensors so that UAVs with advanced computation techniques can be analyzed with different aspects of implementation. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University 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.)

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This scientific article presents a block diagram of a two-channel radio direction finder that effectively uses the phase method to determine the direction of the signal source. The main attention is paid to the mathematical model of the formation of the cardioid radiation pattern of biconical antennas, which have unique directivity characteristics. These features significantly affect the accuracy and reliability of the bearing determination process. The developed algorithm aims to accurately determine the direction of motion of an unmanned aerial vehicle, especially in the context of a twochannel radio receiver and a five-element antenna system. This antenna system provides unique capabilities for increased resolution and directional accuracy. The article also touches on the issue of software implementation of the developed algorithm, which is aimed at increasing the number of generated bearing estimates in conditions of limited time for observing an unmanned aerial vehicle. Thus, the proposed method is of interest in the field of precision direction finding in the context of small unmanned vehicles. [ABSTRACT FROM AUTHOR]

The construction industry is introducing autonomous heavy equipment to overcome labor shortages and improve productivity. For autonomous heavy equipment to work on earthmoving at sites, the equipment needs to recognize and understand ground surface types. However, the ground surface types are manually inspected in practice, and related studies are lacking. To address this issue, the authors developed and tested models that automatically classify ground surface types from images acquired by an unmanned aerial vehicle using a deep learning-based multi-label classification method that applies Binary Relevance (BR) and Label Powerset (LP) methods with Residual Neural Network (ResNet) and Vision Transformer classification network (VIT). The model performances were comparatively evaluated through experiments conducted on actual construction sites. The results showed that the BR model with ResNet is the best model in terms of automated ground surface type identification during earthmoving. The results are expected to broaden the understanding of complex and expansive construction sites for autonomous vehicles and thus facilitate deployment of autonomous heavy equipment by helping them to understand working areas and any obstacles on construction sites quickly and effectively, which will reduce the cost and time needed for on-site ground surface management. [ABSTRACT FROM AUTHOR]

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm's search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions. [ABSTRACT FROM AUTHOR]

Solar energy is playing a crucial role in easing the burden of environmental protection and depletion of conventional energy resources. The use of solar energy in urban settings is essential to meet the growing energy demand and achieve sustainable development goals. This research assesses the solar potential of buildings considering shading events and analyzes the impact of urban built forms (UBFs) on incoming solar potential. The primary data for constructing a virtual 3D city model are derived from a UAV survey, utilizing drone deployment software for flight planning and image acquisition. Geospatial modelling was conducted using the MATLAB Mapping Toolbox to simulate solar irradiation on all the building envelopes in the study area in Jamshedpur, India. The empirical investigation quantified annual solar potential for more than 30,000 buildings in the region by considering time-varying shadowing events based on the sun's path. The region's annual solar energy of 310.149 TWh/year is estimated. Integrating UAV-derived datasets with MATLAB introduces a cost-effective and accurate approach, offering to develop 3D city models, assess solar potential, and correlate the impact of urban building forms (UBFs) to incoming solar potential. [ABSTRACT FROM AUTHOR]

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Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature 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 order to further exploit the production advantages of rice throwing, this paper proposes a systematically designed throwing device suitable for integration with unmanned aerial vehicles (UAVs). The device primarily comprises a seedling carrying and connection system, a seedling pushing mechanism, and a seedling guiding device. The operational principles and workflow of the device are initially elucidated. Subsequently, an analysis of factors influencing rice throwing effectiveness is conducted, with throwing height, working speed, and the bottom diameter of the seedling guide tube identified as key factors. Seedling spacing uniformity and seedling uprightness are designated as performance indicators. A three-factor, three-level response surface experiment is conducted, yielding regression models for the experimental indicators. Through an analysis of the response surface, the optimal parameter combination is determined to be a throwing height of 142.79 cm, a working speed of 55.38 r/min, and a bottom diameter of the seedling guide tube of 43.51 mm. At these parameters, the model predicts a seedling spacing uniformity of 88.43% and a seedling uprightness of 88.12%. Field experiments validate the accuracy of the optimized model results. Experimental data indicate that under the optimal operational parameters calculated by the regression model, the seedling spacing uniformity is 86.7%, and the seedling uprightness is 84.2%. The experimental results meet the design requirements, providing valuable insights for UAV rice-throwing operations. [ABSTRACT FROM AUTHOR]

The article presents comparative results of a real flight experiment and a flight simulation of a small UAV. A longitudinal motion control system is developed using a total energy approach. The outputs of the total energy controller are converted into control signals for deflecting the aircraft rudders in pitch and roll, as well as into engine thrust control. The aircraft motion is modeled as tables of dependencies of the dimensionless coefficients of forces and moments on the angles of attack, slip, and elevon deflection angles. The total energy control method was tested for a small flying wing aircraft. During the simulation, two modes were used: capturing and maintaining altitude and capturing the flight path angle. In the flight experiment, the capturing and maintaining altitude mode was used. The structure of the control system and the results of the simulation and the flight experiment are presented. [ABSTRACT FROM AUTHOR]

Background. This article discusses the problem of navigation and control of unmanned aerial vehicles (UAVs). To ensure the effective performance of the flight task, it is proposed to take into account the calculations of the navigation elements of the flight of an unmanned aerial vehicle, the feature of the proposed method is the ability to evaluate the characteristics and parameters of elements that affect the trajectory and accuracy of flight. Various factors are taken into account, such as the aerodynamic properties of the UAV, environmental conditions, navigation and control systems. Materials and methods. The article presents mathematical models and algorithms for calculating optimal navigation elements, which allow to achieve the best efficiency of the flight task. The proposed approach differs from existing methods in the possibility of more accurate determination of the trajectory and maneuvers of the UAV. Results and conclusions. The results of the study can find practical application in the development of UAV control and navigation systems for various purposes, increasing their accuracy, reliability and efficiency of tasks. The presented methods can be used in civil and military aviation, as well as in other areas where unmanned aerial vehicles are used.

The determination of river velocity is important for hydromorphological analyses and river monitoring systems. Indirect measurements of river velocity using videos recorded by unmanned aerial vehicles (UAV) allow fast and cost-effective processing of information about the river stretch. This paper presents a method for computing flow velocity of the river surface using deep supervised model RAFT to determine the optical flow in combination with image pre-processing by convolutional operations. Moreover, the windiness coefficients and variance score were proposed to evaluate reliability of the collected data and the obtained results of optical flow detection. Various image pre-processing techniques were applied, namely the selection of the analysed area and the number of convolutional operations to select the one with the lowest variance score. This score represents the consistency of the river flow velocity during the video and can be used to filter out unreliable results. The numerical experiments were performed using the videos and directly measured velocity values of 4 shallow rivers in Lithuania collected during the field surveys. The optical velocity estimation method showed good correspondence to the directly measured values for the velocity range from 0 m/s to 0.8 m/s in the points with low variance score up to 0.192 that represents the first quartile of the variance. The optical flow method tends to underestimate the velocity up to 0.5 m/s for the quartiles with the higher variance scores. It was shown that in most cases the lowest variance score value was obtained using pre-processing techniques without convolutional operations. However, the need to analyse various pre-processing techniques arises from the different origin of the objects moving on the river surface.

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers.

Slope shape as a consequence of erosional landform development plays a prominent role in soil erosion. Clarifying the distribution of soil erosion and deposition patterns on different shaped slopes is crucial for soil erosion control. The aim of this study was to decipher the effects of slope shape on soil erosion and deposition patterns under natural rainfall conditions based on high-resolution unmanned aerial vehicle (UAV) data and geographic information system technology. Structure from motion (SfM)-UAV photogrammetry was carried out in four runoff plots with various slope shapes during the rainy season in 2021. Digital elevation models (DEMs) were developed for each slope shape before and after the rainy season. In addition to collecting runoff and sediment, the DEMs of difference were analyzed to quantify soil erosion and deposition patterns on various slope shapes in the rainy season. Results showed that the runoff volumes and sediment yields induced by rainfall were markedly different among various slope shapes. The mean runoff volume and sediment yield from the concave-convex slope were 1.09 ∼ 2.69 and 1.33 ∼ 27.16 times those of the other three slopes, respectively, with less sediment loss from the convex-concave slope and its combination slope. Slope shape exhibited a notable effect on the type of slope erosion and deposition. All four slopes showed considerable changes in surface elevation after the rainy season. The increase and decrease in surface elevation were concentrated in the range of –0.02 to –0.007 m and 0.007 to 0.02 m, respectively, with a low proportion of changes less than –0.03 m and greater than 0.03 m. The effectiveness of SfM-UAV in monitoring the microgeomorphic changes of slopes was verified by the consistency of soil erosion amounts based on sediment collection and SfM-UAV measurements. Reference values were provided to solve the threshold problem of slope length cutoff in soil erosion prediction models based on runoff plot experiments. Findings of this study could be useful for decision-making in soil erosion control and slope reconstruction.

Abstract The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost‐ and time‐efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone‐based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent‐based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems.

This paper aims to clarify the behavior and damage conditions of batteries when multi-rotor unmanned aerial vehicles (drones) in horizontal flight collide with a structure to obtain the engineering knowledge that serves to consider the method of the safety performance evaluation of drone batteries in the collision. Drones with maximum takeoff weight of 15.5 kg, 24.5 kg, and 50 kg for aerial photographing, agricultural chemical spraying, and material transporting were used in the experiment analysis. The drones were ejected from an ejection system and collided with a barrier. The behavior of the drones and the batteries were captured on high-speed cameras. In addition, collision loads on the barrier and accelerations of the drones and their batteries were measured. The behavior of the drones and the batteries were considered based on the analysis results of the high-speed cameras. When the drones collided with the barrier, the batteries attached to the drones with snap-fit, rubber bands, and hook-and-loop fasteners separated from the drones. The separated batteries subsequently collided with the barrier. In the collision of the batteries and the barrier, the acceleration of the battery attached to the front of the drone by snap-fit was maximum. The acceleration was approximately 2.2 times larger than the acceleration given to the battery by the impact testing machine in the JIS standard (JIS C62133-2) that defines the test method for the safe operation of lithium secondary batteries used for portable devices. The damage conditions of batteries were presented and discussed from the viewpoint of the collision safety of batteries.

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

ABSTRACT Agriculture consumes the largest amount of water resources in the world; for this reason, developing technologies aimed at efficiently using these resources for food production is necessary. In the present work, red-green-blue (RGB) and infrared (IR) images of plots with corn (Zea mays L.) were used to estimate the changes in soil moisture. These images were obtained by cameras installed in an unmanned aerial vehicle (drone), which flew over the plots on different dates. The results showed that both RGB and IR images of corn plants can be used to estimate soil moisture with minimum and acceptable levels of root mean square error (with RMSEs of 1.02 and 1.58 for RGB and IR images, respectively); however, the optical response of plants can be altered by different factors in addition to changes in soil moisture; thus, the training of mathematical models to estimate this variable should preferably be performed with validation data at the plot level.

Off-target pesticide drift in paddy fields following unmanned aerial vehicle (UAV) spraying was evaluated using cellulose deposition samplers (CDSs). An analytical method for quantifying ferimzone Z and E isomers deposited on CDSs was developed using LC-MS/MS. The suitability of the CDS method was confirmed by comparing deposition patterns on CDSs with residue levels in rice plant samples. To assess pesticide deposition in paddy fields, CDSs were strategically placed at varying distances from target areas, followed by UAV spraying. The fungicide agrochemicals were applied with and without adjuvants, and wind direction affected the drift trajectory for all treatment groups. Adjuvants, particularly soy lecithin as the major component, significantly enhanced pesticide deposition within the spray pathway while reducing drift rates relatively by 47.9–68.0 %. Higher wind speeds were found to exacerbate drift, but adjuvant-treated sprays showed less variability in deposition patterns under these conditions. Pesticide residues in harvested brown rice were found to be below the maximum residue limits (MRLs), ensuring safety for consumption. These findings highlight the importance of selecting appropriate adjuvants in UAV-based pesticide applications to optimize deposition efficiency and minimize environmental contamination.