Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations.

Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.

A Comparison of Deep Neural Networks for Monocular Depth Map Estimation in Natural Environments Flying at Low Altitude.

Currently, the use of Unmanned Aerial Vehicles (UAVs) in natural and complex environments has been increasing, because they are appropriate and affordable solutions to support different tasks such as rescue, forestry, and agriculture by collecting and analyzing high-resolution monocular images. Autonomous navigation at low altitudes is an important area of research, as it would allow monitoring parts of the crop that are occluded by their foliage or by other plants. This task is difficult due to the large number of obstacles that might be encountered in the drone's path. The generation of high-quality depth maps is an alternative for providing real-time obstacle detection and collision avoidance for autonomous UAVs. In this paper, we present a comparative analysis of four supervised learning deep neural networks and a combination of two for monocular depth map estimation considering images captured at low altitudes in simulated natural environments. Our results show that the Boosting Monocular network is the best performing in terms of depth map accuracy because of its capability to process the same image at different scales to avoid loss of fine details.

Development of an Efficiency Platform Based on MQTT for UAV Controlling and DoS Attack Detection.

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.

Community acceptability of dengue fever surveillance using unmanned aerial vehicles: A cross-sectional study in Malaysia, Mexico, and Turkey.

Surveillance is a critical component of any dengue prevention and control program. There is an increasing effort to use drones in mosquito control surveillance. Due to the novelty of drones, data are scarce on the impact and acceptance of their use in the communities to collect health-related data. The use of drones raises concerns about the protection of human privacy. Here, we show how willingness to be trained and acceptance of drone use in tech-savvy communities can help further discussions in mosquito surveillance. A cross-sectional study was conducted in Malaysia, Mexico, and Turkey to assess knowledge of diseases caused by Aedes mosquitoes, perceptions about drone use for data collection, and acceptance of drones for Aedes mosquito surveillance around homes. Compared with people living in Turkey, Mexicans had 14.3 (p < 0.0001) times higher odds and Malaysians had 4.0 (p = 0.7030) times the odds of being willing to download a mosquito surveillance app. Compared to urban dwellers, rural dwellers had 1.56 times the odds of being willing to be trained. There is widespread community support for drone use in mosquito surveillance and this community buy-in suggests a potential for success in mosquito surveillance using drones. A successful surveillance and community engagement system may be used to monitor a variety of mosquito spp. Future research should include qualitative interview data to add context to these findings.

An Extended Methodology for Sizing Solar Unmanned Aerial Vehicles: Theory and Development of a Python Framework for Design Assist.

There is a growing interest in using unmanned aerial vehicles (UAVs) in the most diverse application areas from agriculture to remote sensing, that determine the need to project and define mission profiles of the UAVs. In addition, solar photovoltaic energy increases the flight autonomy of this type of aircraft, forming the term Solar UAV. This study proposes an extended methodology for sizing Solar UAVs that take off from a runway. This methodology considers mission parameters such as operating location, altitude, flight speed, flight endurance, and payload to sizing the aircraft parameters, such as wingspan, area of embedded solar cells panels, runway length required for takeoff and landing, battery weight, and the total weight of the aircraft. Using the Python language, we developed a framework to apply the proposed methodology and assist in designing a Solar UAV. With this framework, it was possible to perform a sensitivity analysis of design parameters and constraints. Finally, we performed a simulation of a mission, checking the output parameters.

Implementation of Facility Management for Port Infrastructure through the Use of UAVs, Photogrammetry and BIM.

The maintenance of port infrastructures presents difficulties due to their location: an aggressive environment or the variability of the waves can cause progressive deterioration. Maritime conditions make inspections difficult and, added to the lack of use of efficient tools for the management of assets, planning maintenance, important to ensure operability throughout the life cycle of port infrastructures, is generally not a priority. In view of these challenges, this research proposes a methodology for the creation of a port infrastructure asset management tool, generated based on the Design Science Research Method (DSRM), in line with Building Information Modeling (BIM) and digitization trends in the infrastructure sector. The proposal provides workflows and recommendations for the survey of port infrastructures from UAVs, the reconstruction of digital models by photogrammetry (due to scarce technical documentation), and the reconstruction of BIM models. Along with this, the bidirectional linking of traditional asset management spreadsheets with BIM models is proposed, by visual programming, allowing easy visualization of the status and maintenance requirements. This methodology was applied to a port infrastructure, where the methodology demonstrated the correct functionality of the asset management tool, which allows a constant up-dating of information regarding the structural state of the elements and the necessary maintenance activities.

Low-Cost Beamforming Concept for the Control of Radiation Patterns of Antenna Arrays Installed onto UAVs.

This paper presents a low-cost architecture that allows for beamforming with antenna arrays installed onto unmanned aerial vehicles (UAVs). Beam switching is proposed to improve the antenna gain towards the ground station with two three-element arrays installed below the wings of the UAV. The electromagnetic modeling of the complete structure (UAV and integrated antennas) was performed with commercial electromagnetic simulator Ansys HFSS. The radiation patterns were synthesized with particle swarm optimization (PSO). By employing lumped surface-mount device (SMD) components and switches, the design of the feeder to deliver proper excitation coefficients to the antennas is presented, and its performance was assessed by simulations. The proposed approach is demonstrated to be very effective with low-cost production.

Unmanned Aerial Vehicles (UAVs) for Physical Progress Monitoring of Construction.

The physical progress of a construction project is monitored by an inspector responsible for verifying and backing up progress information, usually through site photography. Progress monitoring has improved, thanks to advances in image acquisition, computer vision, and the development of unmanned aerial vehicles (UAVs). However, no comprehensive and simple methodology exists to guide practitioners and facilitate the use of these methods. This research provides recommendations for the periodic recording of the physical progress of a construction site through the manual operation of UAVs and the use of point clouds obtained under photogrammetric techniques. The programmed progress is then compared with the actual progress made in a 4D BIM environment. This methodology was applied in the construction of a reinforced concrete residential building. The results showed the methodology is effective for UAV operation in the work site and the use of the photogrammetric visual records for the monitoring of the physical progress and the communication of the work performed to the project stakeholders.

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview.

Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application's requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

Deep Learning Applied to Vegetation Identification and Removal Using Multidimensional Aerial Data.

When performing structural inspection, the generation of three-dimensional (3D) point clouds is a common resource. Those are usually generated from photogrammetry or through laser scan techniques. However, a significant drawback for complete inspection is the presence of covering vegetation, hiding possible structural problems, and making difficult the acquisition of proper object surfaces in order to provide a reliable diagnostic. Therefore, this research's main contribution is developing an effective vegetation removal methodology through the use of a deep learning structure that is capable of identifying and extracting covering vegetation in 3D point clouds. The proposed approach uses pre and post-processing filtering stages that take advantage of colored point clouds, if they are available, or operate independently. The results showed high classification accuracy and good effectiveness when compared with similar methods in the literature. After this step, if color is available, then a color filter is applied, enhancing the results obtained. Besides, the results are analyzed in light of real Structure From Motion (SFM) reconstruction data, which further validates the proposed method. This research also presented a colored point cloud library of bushes built for the work used by other studies in the field.

Image dataset acquired from an unmanned aerial vehicle over an experimental site within El Soldado estuary in Guaymas, Sonora, México.

It is well known that remote sensing is a series of procedures which detects physical characteristics of the earth surface by remotely-measuring its reflected and emitted radiation using cameras or sensors. Lately, the increasing use of unmanned aerial vehicles (UAVs) as remote sensing platforms and the development of small-size sensors have resulted in the expansion of continuous monitoring of earth surface at smaller spatial scales. For this reason, the integration of UAV- and consumer-grade cameras can be useful to acquire surface characteristics at plot or footprint scale. This dataset contains 314 aerial images covering an area of aproximately 18,800 m2 within the footprint of an Eddy covariance and meterorological station. The monitoring site was deployed at "El Soldado" estuary (27°57'14.4″ N and 110°58'19.2″ W) located in the southern coast of the Mexican State of Sonora. UAV flight path was programmed to flight in autonomous mode with an altitude of 30 m, a velocity of 5 m/s and a frontal and side overlap of 85 and 75% respectively. This dataset was created to support mapping surveys for surface classification and site description. This dataset is aimed to support researchers, stakeholders and general public interested in coastal areas, natural resources management and ecosystem conservation. Finally, this dataset could be also used for those interested in digital photogrammetry and 3D reconstruction as benchmark example to develop high resolution orthomosaics.

Counting Cattle in UAV Images-Dealing with Clustered Animals and Animal/Background Contrast Changes.

The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields accuracies over 90% under a wide variety of conditions and backgrounds.

A Study on the Detection of Cattle in UAV Images Using Deep Learning.

Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (Bos taurus indicus); (2) to determine the ideal ground sample distance (GSD) for animal detection; (3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1853 images containing 8629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures × 3 spacial resolutions × 2 datasets × 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels.

This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7-13 µ m wavelength) from a DJI Matrice 100 1drone (quadcopter). Additionally, our dataset includes the next environmental measurements: temperature, wind speed, and irradiance. The experimental set up consisted in a photovoltaic array of 4 serial monocrystalline Si panels (string) and an electronic equipment emulating a real load. The conditions for images acquisition were stablished in a flight protocol in which we defined altitude, attitude, and weather conditions.

Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array.

Unmanned aerial vehicles (UAV) are growing in popularity, and recent technological advances are fostering the development of new applications for these devices. This paper discusses the use of aerial drones as a platform for deploying a gunshot surveillance system based on an array of microphones. Notwithstanding the difficulties associated with the inherent additive noise from the rotating propellers, this application brings an important advantage: the possibility of estimating the shooter position solely based on the muzzle blast sound, with the support of a digital map of the terrain. This work focuses on direction-of-arrival (DoA) estimation methods applied to audio signals obtained from a microphone array aboard a flying drone. We investigate preprocessing and different DoA estimation techniques in order to obtain the setup that performs better for the application at hand. We use a combination of simulated and actual gunshot signals recorded using a microphone array mounted on a UAV. One of the key insights resulting from the field recordings is the importance of drone positioning, whereby all gunshots recorded in a region outside a cone open from the gun muzzle presented a hit rate close to 96%. Based on experimental results, we claim that reliable bearing estimates can be achieved using a microphone array mounted on a drone.

EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels.

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.

Environmental assessment and historic erosion calculation of abandoned mine tailings from a semi-arid zone of northwestern Mexico: insights from geochemistry and unmanned aerial vehicles.

Mining is known as one of the primary economic activities where exploitation of minerals and other materials have become essential for human development. However, this activity may represent a risk to the environment, starting from deforestation and ending with production of residues that might contain potentially toxic elements. Tailing deposits from historical mining are an example of waste that may represent an environmental concern when abandoned and exposed to environmental conditions. The town of Nacozari de Garcia, in northwestern Mexico, has three abandoned mine tailings (locally known as tailings I, II, and III) located around the urban area that represent important sources of dust and pollution. Images obtained using unmanned aerial vehicles (UAV) in conjunction with geochemical data are used to assess historic erosion calculation and pollution considering contamination and hazard indexes in tailings II and III. Digital elevation models of abandoned tailings were obtained using photogrammetry with UAV. A total of 37 surficial samples were collected from mine tailings to determine elemental concentrations (As, Cu, Pb, W, Zn) using portable X-ray fluorescence. Higher concentrations were found on samples from mine tailing II. Average concentrations followed the decreasing order of Cu > Zn > W > Pb > As for tailing II, whereas decreasing order of Cu > Zn > W > As > Pb was found for tailing III. Contamination Index (CI) values obtained from tailings II and III represent a low potential of pollution, whereas efflorescent crusts from these tailings represent a high potential of polluting soils and sediments by dust generation. Hazard Average Quotient (HAQ) values on both tailings suggest a very high potential of contamination if fluids infiltrate tailings and interact with surficial water and/or groundwater. Obtained surfaces of mine tailings II and III are 146,216 and 216,689 m2, respectively, which represent around 11% of the urbanized area. A loss mass of 321,675 tons was determined for mine tailing II, whereas 634,062 tons for tailing III, accounting for 0.96 million tons of total eroded mass. Since abandonment, calculated erosion rates of 493 t ha-1 year-1 (tailing II) and 232 t ha-1 year-1 (tailing III) are in agreement with those determined in other mining areas. CI and HAQ indexes provide good estimates of pollution associated with abandoned mine tailings from Nacozari de García. Historic erosion determined in these tailings is an environmental concern since eroded material and polluted water have been incorporated into the Moctezuma River, which feeds several villages, whose major activities include agriculture and livestock raising.

The use of unmanned aerial vehicles for health purposes: a systematic review of experimental studies.

BACKGROUND: Unmanned aircraft vehicles (UAVs) have had a rapid escalation in manageability and affordability, which can be exploited in healthcare. We conducted a systematic review examining the use of drones for health-related purposes. METHODS: A search was conducted in Medline, Embase, Global Health, Scopus, CINAHL and SciELO. Experimental studies were selected if the population included human subjects, the intervention was the use of UAVs and there was a health-related outcome. RESULTS: Of 500 results, five met inclusion criteria during an initial search. An updated search yielded four additional studies. Nine studies, all in high-income countries, were included for systematic syntheses: four studies addressed out-of-hospital cardiac arrest emergencies, three assessed drones for identification of people after accidents, one used drones to transport blood samples and one used drones to improve surgical procedures in war zones. CONCLUSIONS: Research on the use of drones in healthcare is limited to simulation scenarios, and this review did not retrieve any studies from low- and middle-income countries.

Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks.

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R² values of greater than 0.60, consistent with literature values.

Dual-Stack Single-Radio Communication Architecture for UAV Acting As a Mobile Node to Collect Data in WSNs.

The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.