Use Of Unmanned Aerial Vehicles Research Articles

An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles

Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, and more intelligent. With the widespread use of unmanned aerial vehicles (UAVs), conducting railway inspections using lightweight, low-power devices carried by UAVs has become a future trend. In this paper, we address the characteristics of track fastener detection tasks by improving the YOLOv4-tiny object detection model. We improved the model to output single-scale features and used the K-means++ algorithm to cluster the dataset, obtaining anchor boxes that were better suited to the dataset. Finally, we developed the FPGA platform and deployed the transformed model on this platform. The experimental results demonstrated that the improved model achieved an mAP of 95.1% and a speed of 295.9 FPS on the FPGA, surpassing the performance of existing object detection models. Moreover, the lightweight and low-powered FPGA platform meets the requirements for UAV deployment.

Unmanned aerial vehicle as an air defense target

There have been reports of unauthorized use of unmanned aerial vehicles (UAVs) in highly controlled areas (airports, military installations, against critical industrial infrastructure) in the media since the mid-2000s. Nowadays, small UAVs are widely used for unauthorized surveillance of important objects, conducting terrorist attacks and sabotage, carrying prohibited goods (weapons, drugs), as well as for military purposes. For this reason, the problem of countering UAVs, and especially small UAVs, has become extremely relevant. Analysis of publications in this area has shown a small number of serious studies on this topic. There are often too optimistic conclusions about the effectiveness of existing air defense systems for countering all types of UAVs in many papers. However, the problem of countering UAVs, and especially small UAVs, is highly complex, multi-faceted, and has not been solved yet. The purpose of this paper is to systematize and analyze various ways and means of countering UAVs, as well as to form general directions for effective solution of the problem. The material presented in the paper focuses on the analysis of the capabilities of air defense means to physical countering UAVs. The results of systematization and analysis of various methods and means of countering UAVs, which are based on physical interception, are presented in the article. This systematization is based on more than 60 open sources. The analysis of the sources show the main features of the UAV as an object of destruction and made possible a detailed multi-aspect analysis of modern air defense systems, their effectiveness and disadvantage. Suggestions for improving the effectiveness of the air defense systems what are used against UAVs are also summarized in this paper. Elements of the novelty of the paper are the general features of the detection and destruction of UAVs as well as systemic disadvantage in technological solutions of the air defense systems which lead to a decrease in their combat effectiveness when they are used against UAVs. The material of the paper can be used to generate initial data for modeling and studying the combat effectiveness of the air defense systems when countering UAVs. This article can be useful for designers who design countering UAV systems as well.

Unmanned aerial vehicle as a reconnaissance object

There have been reports of unauthorized use of unmanned aerial vehicles (UAVs) in highly controlled areas (airports, military facilities, against critical industrial infrastructure) in the media since the mid-2000s. Nowadays, small UAVs are widely used for unauthorized surveillance of important objects, conducting terrorist attacks and sabotage, transporting prohibited goods (weapons, drugs), as well as for military purposes. For this reason, the problem of countering UAVs, and especially small UAVs, has be-come extremely relevant. Analysis of publications in this area has revealed a small number of serious studies in this topic. Conclusions, made on the effectiveness of existing air defense systems for combating all types of UAVs in several papers seem to be too optimistic. However, the problem of countering UAVs, and especially small UAVs, is highly complicated, multifaceted and has not been solved yet. The goal of this paper is to analyze UAV as an object of detection and defeating while using various ways and means of countering UAVs. This work focuses on the analysis of UAV as an object of radar, radio-reconnaissance and radio-technical, optical-electronic and acoustic intelligence, as well as an object of fire and electronic defeating. Results of systematization and analysis of UAVs as an object of fire and electronic defeating, capabilities of on time detecting and target indicating by radar, radio-reconnaissance and radio-technical, optical-electronic and acoustic means of intelligence are presented in the paper. Carried out systematization is based on information from more than 40 open sources. Analysis of the sources reveals the list of main features of the UAV as an object of defeating, and makes it possible to carry out a detailed analysis of modern detection systems, as well as their effectiveness and disadvantages. Elements of novelty of the paper are general features of UAVs detection process, as well as systemic disadvantages of the detection systems technical solutions, which lead to reduce in efficiency, while being used against UAVs. The material of the paper can be used to generate initial data for modeling and studying the combat effectiveness of the air defense systems when countering UAVs. This article can be useful for constructors, who design the countering UAV systems.

Tank-mix adjuvants improved spray performance and biological efficacy in rice insecticide application with unmanned aerial vehicle sprayer.

The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank-mix adjuvants, Wonderful Rosin (Adjuvant-1) and Tiandun (Adjuvant-2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant-1 and Adjuvant-2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant-1) and 20.23% (Adjuvant-2) over applications without tank-mix adjuvant 7 days after treatment. The judicious selection of tank-mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank-mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry.

Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R2) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.

Application of UAV 3D Point Cloud Data in Highway Slope Disaster Investigation

The safety and stability of highway slopes have always been critical concerns in highway management and maintenance. However, mountain road slopes, characterized by their small scale, large quantity, and complex geological conditions, pose challenges for effective deformation detection, putting significant pressure on highway management and maintenance. To address the difficulty in identifying hidden hazards of highway slopes in mountainous areas, this study proposes the use of unmanned aerial vehicle (UAV) closed-range photogrammetry technology for slope inspection. The M3C2 (Multiscale Model-to-Model Cloud Comparison) algorithm is employed to accurately calculate differences in multi-phase three-dimensional point cloud data, enabling the detection of overall slope deformation with centimeter-level precision. Field verification conducted on a highway slope in Qingtian County, Zhejiang Province, demonstrated the applicability of UAV 3D point cloud data in investigating highway slope hazards, thereby enhancing inspection efficiency and quality and providing technical support for highway management and maintenance.

Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-Based Plant Height Dynamics for Breeding

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height (PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle (UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading (IH) and full heading (FH), and panicle initiation (PI), and growth period after transplanting (GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model (DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest (RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th (R2 = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features (CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI (R2 = 0.834, RMSE = 4.344 d), IH (R2 = 0.877, RMSE = 2.721 d), and FH (R2 = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

Evaluation of the barriers to and drivers of the incorporation of unmanned aerial vehicles into dam asset management

ABSTRACT Dam asset management entails the consistent evaluation of the state and usefulness of dams as tangible assets in terms of their expected lifespan, criticality, operational history, maintenance history, and long-term financing plan. This research aims to identify and evaluate the critical drivers and barriers associated with the use of unmanned aerial vehicles in dam asset management. This study uses rough decision-making trial and evaluation laboratory and interpretive structure modelling to analyze the interactions that take place between the barriers to and the drivers of unmanned aerial vehicles incorporation. To overcome the problem of vagueness, it develops rough set theory to determine the driving and dependence power of the drivers and the barriers, respectively. According to the findings, the most significant barrier to the incorporation of unmanned aerial vehicles into dam asset management is a lack of skilled operators, while the most significant driver of their incorporation is their cost-effectiveness. The findings of this study will be highly valuable to practitioners and government agencies working on the implementation of unmanned aerial vehicles in dam asset management, as they will enable them to correctly assess the different aspects of unmanned aerial vehicles incorporation.

Identifying Characteristic Fire Properties with Stationary and Non-Stationary Fire Alarm Systems.

The article reviews issues associated with the operation of stationary and non-stationary electronic fire alarm systems (FASs). These systems are employed for the fire protection of selected buildings (stationary) or to monitor vast areas, e.g., forests, airports, logistics hubs, etc. (non-stationary). An FAS is operated under various environmental conditions, indoor and outdoor, favourable or unfavourable to the operation process. Therefore, an FAS has to exhibit a reliable structure in terms of power supply and operation. To this end, the paper discusses a representative FAS monitoring a facility and presents basic tactical and technical assumptions for a non-stationary system. The authors reviewed fire detection methods in terms of fire characteristic values (FCVs) impacting detector sensors. Another part of the article focuses on false alarm causes. Assumptions behind the use of unmanned aerial vehicles (UAVs) with visible-range cameras (e.g., Aviotec) and thermal imaging were presented for non-stationary FASs. The FAS operation process model was defined and a computer simulation related to its operation was conducted. Analysing the FAS operation process in the form of models and graphs, and the conducted computer simulation enabled conclusions to be drawn. They may be applied for the design, ongoing maintenance and operation of an FAS. As part of the paper, the authors conducted a reliability analysis of a selected FAS based on the original performance tests of an actual system in operation. They formulated basic technical and tactical requirements applicable to stationary and mobile FASs detecting the so-called vast fires.

Reactive UAV-based automatic tunnel surface defect inspection with a field test

This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions.

Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.

Detection of pine wood nematode infestation using hyperspectral drone images

The pine wood nematode (PWN) is one of the most threatening pests of pine forests in China. An essential management response involves felling and removing the infested trees before the PWN can spread over large areas, but this requires early detection. In this study, we analyzed the temporal changes in the spectral signatures of healthy and infested trees throughout the entire growing season (May to October) using unmanned aerial vehicle (UAV) hyperspectral images (HSI). To explore the potential of these seasonal spectral changes in detecting PWN, we investigated the separability of healthy and infested trees using both absolute values and changes in values. A random forest (RF) classifier was applied to separate healthy and infested trees based on reflectances, derivatives, vegetation indices (VIs), and their seasonal change values. We obtained the following results: (1) the seasonal changes of tree crown spectra following a PWN infestation were similar to those observed using absolute spectral values, though the latter performed better in early detection; (2) in the early stage of infestation (June and July), the green and red-edge regions contained the most sensitive bands, whereas the near-infrared portion played a more important role in later stages; (3) derivatives achieved higher overall accuracy (OA) than the reflectance in the early stage of PWN infestation (June and July); (4) in June, the Pigment Specific Simple Ratio (PSSR) had the highest separability (OA = 0.77) for PWN infestation, followed by the Plant Senescence Reflectance Index (PSRI) and the Normalized Difference Red-edge Index (NDRE), both with a OA of 0.70. In July, PSRI, Red Edge Normalized Difference Vegetation Index (RENDVI), and the Modified Red Edge Simple Ratio (MRESR) had the highest OA of 0.77. These VIs were calculated based on the red-edge regions. In August, RENDVI achieved the best accuracy (0.87), followed by the Normalized Difference Vegetation Index (NDVI, OA = 0.83) and the Plant Stress Index (PSI, OA = 0.80). In September and October, PSRI, PSI, the Greenness Index (GI), and RENDVI showed the strongest separability (0.87 to 0.90). Similar results were obtained using change values of VIs. In June, RENDVI, PSI, and NDVI showed the highest accuracy (OA = 0.73). In July, MRESR had the best accuracy (0.77), followed by RENDVI (0.73) and PSI (0.73). In the late stage of PWN infestation, NDVI, PSRI, PSI, RENDVI, GI, and PSSR obtained the highest accuracies (0.83 to 0.93), surpassing the accuracy achieved using absolute spectra; (5) in RF classification model, seasonal variation values derived from reflectances, derivatives, and VIs performed well in the late stages of the PWN infestation (September and October) but were not as good as absolute values in the early and middle stages (June to August). Our study explored the potential of seasonal changes in UAV spectra for early detection of forest pests.

EXAMINING ARCHITECTURAL PROJECT APPLICATIONS WITH PHOTOGRAMMETRIC POINT CLOUD: IĞDIR UNIVERSITY MOSQUE

In accordance with the developments in today's technology, the use of unmanned aerial vehicles in architectural documentation and 3D modeling studies is becoming increasingly widespread. When the studies in the literature are examined, it is seen that they focus on the documentation and 3D modeling of historical buildings. In this study, unlike the research in the literature, the usability of UAV photogrammetry in architectural project implementation and monitoring is investigated. In line with this aim, 3D point clouds and 3D models were generated using UAV photos in the study. For the identified reference points, measurements obtained from architectural project drawings, measurements taken on-site using the measurement technique with a laser meter, and finally the comparison of the measurements corresponding to these reference points on the 3D model produced from UAV photos were conducted. Based on the obtained data, when the detailed measurements taken with a laser meter are compared with the detailed measurements taken from the 3D model obtained with UAV, the consistency level is observed to be an average of 98% and above. Thus, the accuracy of the measurements obtained using UAV photogrammetry has been proven. Additionally, it was observed that measurements taken with a laser meter from the same reference points on the architectural project were different. The reason for this is the modification of the forms of details in the implementation phase of the architectural project. In this regard, it is thought that obtaining precise measurements on photos taken with UAV photogrammetry will provide an important groundwork for future studies on architectural project implementation and monitoring.

Investigation of the effect of object size on accuracy of human localisation in images acquired from unmanned aerial vehicles

The use of unmanned aerial vehicles is gaining wide popularity in various areas of research and information acquisition. More and more often, unmanned aerial vehicles are used to obtain various types of images of the Earth’s surface for its study. In particular, such data can be used in law enforcement, localization of crowds, etc. Typically, such systems operate independently of humans and provide information about objects in an automatic mode, with humans working only under the control of the aircraft. One of the main components of such systems is a neural network for localization and classification of objects, the parameters of which determine both the accuracy of the system as a whole and the design of the aircraft for shooting. In particular, the accuracy of the neural network determines the profitability of such a system, because if the accuracy is insufficient, the use of such systems will not make sense. Therefore, the main subject of research in this paper is a neural network for object localization, in particular YOLO v5, and its accuracy parameters on images obtained from unmanned aerial vehicles. The main focus of this paper is on the parameters of the neural network and the study of its metrics, which are important parameters of a trained neural network. Another important parameter for the further use of a neural network is its training parameters, as well as the data used for training. This study also pays attention to the details of the training process. The main goal of this study is to train a neural network on a selected dataset and to study the accuracy metrics of the trained neural network. The main goal of this study is to determine the dependence of localization accuracy on the area of the object, which will allow for more detailed development of unmanned systems with automatic object detection, as well as to assess the profitability of using such systems in task planning. On the basis of the data obtained, conclusions were drawn about the dependence of localization accuracy on the area of an object in images from unmanned aerial vehicles. These data can serve as a reference for unmanned aerial vehicle developers, particularly when selecting photo modules or planning the system architecture.

First use of unmanned aerial vehicles to monitor Halyomorpha halys and recognize it using artificial intelligence.

Halyomorpha halys is one of the most damaging invasive agricultural pests in North America and southern Europe. It is commonly monitored using pheromone traps, which are not very effective because few bugs are caught and some escape and/or remain outside the trap on surrounding plants where they feed, increasing the damage. Other monitoring techniques are based on visual sampling, sweep-netting and tree-beating. However, all these methods require several hours of human labor and are difficult to apply to large areas. The aim of this work is to develop an automated monitoring system that integrates image acquisition through the use of drones with H. halys detection through the use of artificial intelligence (AI). The study results allowed the development of an automated flight protocol using a mobile app to capture high-resolution images. The drone caused only low levels of disturbance in both adult and intermediate instars, inducing freezing behavior in adults. Each of the AI models used achieved very good performance, with a detection accuracy of up to 97% and recall of up to 87% for the X-TL model. The first application of this novel monitoring system demonstrated the potential of drones and AI to detect and quantify the presence of H. halys. The ability to capture high-altitude, high-resolution images makes this method potentially suitable for use with a range of crops and pests. © 2024 Society of Chemical Industry.

Mapping large-scale pine wilt disease trees with a lightweight deep-learning model and very high-resolution UAV images

ABSTRACT Pine wilt disease (PWD), caused by pine wood nematodes, has brought a great loss in ecology and economy all over the world. In China, the forest health status is also significantly affected by PWD since 1980, especially in coniferous forests and mixed forest regions. The PWD spreads very fast and can cause a healthy pine wood tree to die within a very short time. An effective way to protect other healthy pine wood trees is to discover PWD trees early. Using unmanned aerial vehicle (UAV) images can help people discover the PWD tree quickly and accurately, and a few automatical methods have been developed to monitor PWD trees including the deep learning methods. Because of the robust spatial-temporal transferability, deep learning methods have become the mainstream algorithms to monitor PWD trees. As we know, the training dataset is the most important material to train a deep learning model. However, there is still a lack of a PWD segmentation dataset so far. To fill this gap, in this paper, we have generated the first open-sourced PWD segmentation dataset based on very high-resolution UAV images to help the community conduct PWD monitor research conveniently. This dataset has 994 training samples, and each sample has visible bands with 512 × 512 pixels, and the spatial resolution of this dataset is 0.05 m. In order to train an advanced segmentation model, we have designed a lightweight deep-learning model for mobile devices or edge devices in this manuscript, named MobileSeg. The main feature of MobileSeg is its decoupling, which uses the re-parameterization technology to improve the model performance. Finally, a large-scale real-world scenario experiment with very high-resolution UAV images was utilized to validate the performance of MobileSeg, the experiment result indicated that MobileSeg has achieved the best performance compared with the recent lightweight segmentation models, and the experiment also proved the effectiveness of the proposed PWD dataset.

Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

AbstractOver the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R2 = 0.99, RMSE = 0.09 plants/m2). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R2 = 0.76, RMSE = 0.67 plants/m2). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases.

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

THE PROCEDURE FOR THE USE OF UNMANNED AERIAL VEHICLES TO CONDUCT RECONNAISSANCE ON THE GROUND IN WARTIME CONDITIONS

The article reveals the peculiarities of using unmanned aerial vehicles for reconnaissance in the field in wartime.
 The article reveals the essence and content of the use of unmanned aerial vehicles for reconnaissance, as well as the conditions and features of their effective use.
 Study of the possibilities of using unmanned to conduct reconnaissance on the ground in wartime conditions has shown that this technology has great potential to improve operational intelligence and ensuring the safety of military personnel. The advantages of using unmanned aerial vehicles for reconnaissance are obvious. They can quickly and efficiently penetrate enemy territory, collect information territory, collect information and transmit it in real time. This makes it possible to Reduce the risk to the lives of servicemen, as they do not need to go to the front line the front line to conduct reconnaissance. In addition, unmanned aerial vehicles can operate in hazardous environments, such as radioactive contamination or chemicals where humans cannot enter. The use of unmanned aerial vehicles also ensures greater accuracy and objectivity in collecting information. They are equipped with various types of sensors, such as high-resolution cameras, thermal cameras, radars, etc., which provide detailed information about the territory, enemy positions and movements. This significantly improves the quality of intelligence and allows for more informed decisions on further actions.
 In summary, research has shown that the use of unmanned aerial vehicles for reconnaissance in wartime has significant potential. They can collect information quickly, efficiently and safely, improving operational intelligence and ensuring the safety of soldiers. However, further research and development of new technologies is needed to improve the functionality and safety of unmanned aerial vehicles in warfare.