Use Of Unmanned Aerial Vehicles Research Articles

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.

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 as few bugs are caught, 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 labour and are difficult to apply to large areas. The aim of this work is to develop an automated monitoring system that integrates the acquisition of images through the use of drones and the detection of H. halys through the use of artificial intelligence. The results allowed the development of an automated flight protocol using a mobile app to capture high-resolution images. The drone showed low disturbance in both adult and intermediate instars, inducing freezing behavior in adults. Each of the artificial intelligence models used achieved very good performance, with 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 has demonstrated the potential of drones and artificial intelligence to detect and quantify the presence of H. halys. The ability to capture high-altitude, high-resolution images makes this method potentially suitable for a range of crops and pests.

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.

Particle size distribution (PSD) estimation using unmanned aerial vehicle (UAV) photogrammetry for rockfill shear strength characterization

AbstractThe strength of rockfills and waste materials is significantly influenced by their particle size distribution (PSD). For large waste rockfills, PSD is fundamental to determine the shear behavior. Traditionally, PSD for rockfill, used in materials like coarse-grained aggregates, has been obtained through physical sieving. However, the particle sizes in hard rockfills can vary significantly from small particles (< 20 cm diameter) to large blocks or boulders over 100 cm, with the maximum size limited by the in situ ground conditions and blasting performance. Essentially, the sieving process is impractical, considering the scale of the mine waste dumps and the time required. Therefore, in this study, a workflow using digital detection to estimate the PSD is presented, aiming to quantify the waste dump shear strength using Barton–Kjaernsli empirical criterion. PSD from UAV is validated using manual field measurements of individual boulders. The error for coarse characteristic size prediction ranges within ± 4 mm, and the increase in the data collection frequency, area covered, and resolution of fragmentation measurement for rockfills and waste dumps using UAV allows to improve the statistical reliability of the PSD and fragmentation measurement.

Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade, the presence of mistletoe (Viscum album ssp. austriacum) in Scots pine stands has increased in many European countries. Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future. Therefore, the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age, top height, and stand density, as well as topographic and edaphic factors. We used unmanned aerial vehicle (UAV) imagery from 2247 stands to detect mistletoe in Scots pine stands, while majority stand and site characteristics were calculated from airborne laser scanning (ALS) data. Information on stand age and site type from the State Forest database were also used. We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics. We documented that the densest, tallest, and oldest stands were more susceptible to mistletoe infestation. Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence. In addition, climatic water balance was a significant factor in increasing the probability of mistletoe occurrence, which is important in the context of predicted temperature increases associated with climate change. Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change. In an era of climate change and technological development, the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.

Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks

High-resolution (HR) crack images have proven valuable for bridge inspection using unmanned aerial vehicles (UAVs), offering fine details crucial for accurate segmentation. Traditional deep learning (DL) struggles with HR images due to downsampling issues and limited computational resources. To address this, we propose Cascade-FcaHRNet, a HR representation learning-based multiscale architecture. It incorporates a frequency-channel attention mechanism to capture tiny crack features, a two-stage cascade operation for global and local refinement, and a region-sensitive loss to avoid ambiguous predictions. Ablation studies confirm the effectiveness of these modifications. Robustness experiments show improvements in performance metrics for crack segmentation. In a field test, the Cascade-FcaHRNet accurately segments bridge cracks wider than 0.5 mm from 4 K resolution images, enhancing safety and efficiency in UAV-based bridge inspection. The approach holds potential for developing scientifically sound maintenance and management strategies.

Detailed Analysis of Li-ion Batteries for Use in Unmanned Aerial Vehicles

With the developing technologies in the aviation, the transition to more electrical systems is increasing day by day. For this reason, research on the development of batteries has accelerated. Nowadays, Lithium ion (Li-ion) batteries are more widely preferred due to their energy-to-weight ratio and advantages such as having a lower self-discharge rate when not working compared to other battery technologies. Batteries convert the stored chemical energy into electrical energy and heat is released as a result of the chemical reactions. The heat released negatively affects the battery's lifespan, charging/discharging time and battery output voltage. The battery must be modeled correctly to see these negative effects and intervene in time. In this way, negative situations that may occur in the battery can be intervened at the right time without any incident. 
 In this study, the unmanned aerial vehicle (UAV) is powered by Li-ion batteries. It is simulated in Matlab/Simulink environment using the electrical equivalent circuit. A detailed model is created, taking into account temperature, state of charge (SoC), cell dynamics and operating functions. To estimate state of health (SoH) of the battery, resistance values must be known. Resistance and capacity values in the equivalent circuit of the Li-ion battery are obtained with the help of the simulation model. So, the SoH of the Li-ion batteries can be accurately predicted with the results obtained.

HUMAN DETECTION IN SEARCH AND RESCUE OPERATIONS USING EMBEDDED ARTIFICIAL INTELLIGENCE

The paper discusses the use of unmanned aerial vehicles (drones) in search and rescue operations to detect humans in disaster areas where rescue teams cannot reach. The paper highlights the limitations of current methods, including high computational power, high cost, and dependence on internet connectivity. The paper proposes using transfer learning to develop a human detection model with a mean average precision (mAP@0.5) above 90% and compares two deep learning models, MobileNet v2 and EfficientDet. The study uses multi-datasets of aerial images of humans, namely SeaDronesee and SARD, and the TensorFlow version 2.8 framework. MobileNet v2 required less GPU usage for training and yielded a relatively high accuracy of 95.5%, while EfficientDet achieved higher accuracy (97.3%). The trained MobileNet v2 model size is compressed using quantization from 25.5 MB to 4.15 MB, making it suitable for deployment on an edge device for on-chip inference. The paper concludes that the proposed method can improve the efficiency and effectiveness of search and rescue operations.

Energy-Efficient Device-to-Device Communications for Green Internet of Things Using Unmanned Aerial Vehicle-Mounted Intelligent Reflecting Surface

The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.

LSNetv2: Improving weakly supervised power line detection with bipartite matching

This paper addresses the crucial task of power line detection and localization in electrical infrastructure inspection using Unmanned Aerial Vehicles (UAVs) from weak supervision, polyline annotations. We first identify several limitations in the state-of-the-art approach LSNet. In particular, the inability of LSNet to detect line-crossings and lines in close proximity. To overcome these limitations, we propose LSNetv2, which enhances LSNet with multi-line segment detection capability facilitated via a bipartite matching loss. Additionally, we update LSNet’s regression loss in order to stabilize training by reducing the interdependence between predicted coordinates. Finally, LSNetv2 makes use of an increased receptive field to extract global information, improving overall detection performance. Through extensive evaluations on various power line detection datasets, LSNetv2 demonstrates superior performance and robustness. On the public datasets PLDU, PLDM and TTPLA, it achieved Fβ scores of 0.857, 0.875, and 0.671, respectively, while using only modified weak polyline annotation, establishing itself as an effective and efficient solution for power line detection in UAV-based electrical infrastructure inspections.

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.

Multi-attribute, graph-based approach for duplicate cattle removal and counting in large pasture areas from multiple aerial images

In this study, we address the challenges of counting cattle in large pasture areas using Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras. Traditional manual counting methods are laborious and error-prone, while existing automated approaches struggle with duplicate animal detection. To overcome these limitations, we propose a novel graph-based method that incorporates multiple-attributes, including velocity, direction, state (lying down or standing), color, and distance, to improve duplicate removal and counting accuracy. We conducted extensive experiments involving automated hyper-parameter learning to effectively integrate these attributes into our method. By employing a Ford–Fulkerson graph algorithm, we detect and remove duplicated cattle based on their multiple attributes. An ablation study validates the contribution of each attribute. Additionally, we provide new datasets of drone images captured in large pastures to support research in this field. Our results demonstrate that our proposed method outperforms state-of-the-art techniques, achieving an average percentage error of 2.34%. Comparisons with mosaic-based and other graph-based methods validate the effectiveness of our approach, which contributes to more efficient cattle counting practices and enhances livestock management in agriculture.

Hybrid Encryption for Securing and Tracking Goods Delivery by Multipurpose Unmanned Aerial Vehicles in Rural Areas Using Cipher Block Chaining and Physical Layer Security

This paper investigated the use of unmanned aerial vehicles (UAVs) for the delivery of critical goods to remote areas in the absence of network connectivity. Under such conditions, it is important to track the delivery process and record the transactions in a delay-tolerant fashion so that this information can be recovered after the UAV’s return to base. We propose a novel framework that combines the strengths of cipher block chaining, physical layer security, and symmetric and asymmetric encryption techniques in order to safely encrypt the transaction logs of remote delivery operations. The proposed approach is shown to provide high security levels, making the keys undetectable, in addition to being robust to attacks. Thus, it is very useful in drone systems used for logistics and autonomous goods delivery to multiple destinations. This is particularly important in health applications, e.g., for vaccine transmissions, or in relief and rescue operations.

Lessons learned from a UAV survey and methane emissions calculation at a UK landfill

Accurate quantification of methane emissions from landfills is crucial for improving greenhouse gas inventories and mitigating climate change impacts. Existing methodologies, such as theoretical gas production models and labour-intensive measurement approaches, present limitations including large uncertainties and high operational costs. This study adds to a growing body of research and applications which aim to bridge this gap. To this end, we present a case study using Unmanned Aerial Vehicles (UAVs) equipped with methane and wind instrumentation for a survey of a landfill site in Bury, Manchester, UK, in summer 2022, in order to evaluate and reflect the challenges of the UAV-based mass balance method for quantification of methane emissions from a large heterogeneous source such as landfill. This study offers guidance on defining an appropriate methane background concentration, geospatial interpolation of sampled date, survey sampling strategy, and more importantly, addresses the challenges surrounding UAV wind measurements and spatial characterisation of emission plumes. For the period of the case study, we quantified methane flux for the landfill site to be 150.7 kg h−1 with a 1 standard deviation uncertainty range of 83.0 kg h−1 to 209.5 kg h−1.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

Evaluating the use of unmanned aerial vehicles for spray applications in mountain Nanguo pear orchards.

Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling. Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling. UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry.