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This article presents a systematic literature review of technologies and solutions for cattle tracking and monitoring based on a comprehensive analysis of scientific articles published since 2017. The main objective of this review is to identify the current state of the art and the trends in this field, as well as to provide a guide for selecting the most suitable solution according to the user's needs and preferences. This review covers various aspects of cattle tracking, such as the devices, sensors, power supply, wireless communication protocols, and software used to collect, process, and visualize the data. The review also compares the advantages and disadvantages of different solutions, such as collars, cameras, and drones, in terms of cost, scalability, precision, and invasiveness. The results show that there is a growing interest and innovation in livestock localization and tracking, with a focus on integrating and adapting various technologies for effective and reliable monitoring in real-world environments.

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The massive arrival of pelagic Sargassum on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of Sargassum in the ocean, coasts, and beaches is necessary to understand the phenomenon and develop protocols for its management, use, and final disposal. In this context, the present study proposes a methodology to calculate the area Sargassum occupies on beaches in square meters, based on the semantic segmentation of aerial images using the pix2pix architecture. For training and testing the algorithm, a unique dataset was built from scratch, consisting of 15,268 aerial images segmented into three classes. The images correspond to beaches in the cities of Mahahual and Puerto Morelos, located in Quintana Roo, Mexico. To analyze the results the fß-score metric was used. The results for the Sargassum class indicate that there is a balance between false positives and false negatives, with a slight bias towards false negatives, which means that the algorithm tends to underestimate the Sargassum pixels in the images. To know the confidence intervals within which the algorithm performs better, the results of the f0.5-score metric were resampled by bootstrapping considering all classes and considering only the Sargassum class. From the above, we found that the algorithm offers better performance when segmenting Sargassum images on the sand. From the results, maps showing the Sargassum coverage area along the beach were designed to complement the previous ones and provide insight into the field of study.

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Geoffroy's spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission-fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (-45°, -90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was "substantial" (Fleiss' kappa coefficient = 0.61-0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a -90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.

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Mangrove canopy height (MCH) has been described as a leading characteristic of mangrove forests, protecting coastal economic interests from hurricanes. Meanwhile, winter temperature has been considered the main factor controlling the MCH along subtropical coastlines. However, the MCH in Cedar Key, Florida (∼12 m), is significantly higher than in Port Fourchon, Louisiana (∼2.5 m), even though these two subtropical locations have similar winter temperatures. Port Fourchon has been more frequently impacted by hurricanes than Cedar Key, suggesting that hurricanes may have limited the MCH in Port Fourchon rather than simply winter temperatures. This hypothesis was evaluated using novel high-resolution remote sensing techniques that tracked the MCH changes between 2002 and 2023. Results indicate that hurricanes were the limiting factor keeping the mean MCH at Port Fourchon to <1 m (2002-2013), as the absence of hurricane impacts between 2013 and 2018 allowed the mean MCH to increase by 60 cm despite the winter freezes in Jan/2014 and Jan/2018. Hurricanes Zeta (2020) and Ida (2021) caused a decrease in the mean MCH by 20 cm, breaking branches, defoliating the canopy, and toppling trees. The mean MCH (∼1.6 m) attained before Zeta and Ida has not yet been recovered as of August 2023 (∼1.4 m), suggesting a longer-lasting impact (>4 years) of hurricanes on mangroves than winter freezes (<1 year). The high frequency of hurricanes affecting mangroves at Port Fourchon has acted as a periodic "pruning," particularly of the tallest Avicennia trees, inhibiting their natural growth rates even during quiet periods following hurricane events (e.g., 12 cm/yr, 2013-2018). By contrast, the absence of hurricanes in Cedar Key (2000-2020) has allowed the MCH to reach 12 m (44-50 cm/yr), implying that, besides the winter temperature, the frequency and intensity of hurricanes are important factors limiting the MCH on their latitudinal range limits in the Gulf of Mexico.

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Leucoptera coffeella (Lepidoptera: Lyonetiidae) is one of the main pests in coffee crops. The economic injury level (EIL) is the lowest density of the pest at which economic damages match the costs of control measures. The economic threshold (ET) is the density of the pest at which control measures must be taken so that this population does not reach the EIL. These are the main indices used for pest control decision-making. Control of L. coffeella is carried out by manual, tractor, airplane or drone applications. This work aimed to determine EILs and ETs for L. coffeella as a function of insecticide application technology in conventional and organic Coffea arabica crops. Data were collected over five years in commercial C. arabica crops on seven 100 ha central pivots. The cost of control in organic crops was 16.98% higher than conventional. The decreasing order of control cost was manual > drone > airplane > tractor application. Coffee plants were tolerant to low densities (up to 15% mined leaves) of the pest that caused losses of up to 6.56%. At high pest densities (54.20% mined leaves), losses were high (85.62%). In organic and conventional crops and with the use of different insecticide application technologies, EIL and ET were similar. The EIL and ET were 14% and 11% of mined leaves, respectively. Therefore, these indices can be incorporated in integrated pest management programs in C. arabica crops. The indices determined as a function of insecticide application technology in organic and conventional coffee are important as they serve producers with different technological levels. Additionally, EILs and ETs can contribute to more sustainable production, as control methods will only be employed when the pest density reaches these indices.

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Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone flight plans. To address this, we introduce SiaN-VO, a Siamese neural network designed for visual odometry prediction in such challenging scenarios. Our preliminary studies have shown promising results, particularly for flights under static conditions (constant speed and altitude); while these findings are encouraging, they do not fully represent the complexities of real-world flight conditions. Therefore, in this paper, we have furthered our research to enhance SiaN-VO, improving data integration from multiple sensors and enabling more accurate displacement predictions in dynamic flight conditions, thereby marking a significant step forward in drone navigation technology.

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This article presents a dataset of thermographic images of terrain with antipersonnel mines to identify the presence or absence of these artifacts using machine learning and artificial vision techniques. The dataset has 2700 thermographic images acquired at different heights, using a Zenmuse XT infrared camera (7-13 µm), embedded in the DJI Matrice 100 drone. The data acquisition experiment consists of capturing aerial infrared images of a terrain where elements with characteristics similar to antipersonnel mines type legbreaker were buried. The mines were planted in the ground between 0 cm and 10 cm deep and were spread over an area of 10 m x 10 m. The drone used a flight protocol that set the trajectory, the time of the flight, the acquisition height, and the image sampling frequency. This dataset was used in "Detection of "legbreaker" antipersonnel landmines by analysis of aerial thermographic images of the soil" [7].

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Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

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Predictions of the effects of modern Relative Sea-Level (RSL) rise on mangroves should be based on decadal-millennial mangrove dynamics and the particularities of each depositional environment under past RSL changes. This work identified inland and seaward mangrove migrations along the Ceará-Mirim estuary (Rio Grande do Norte, northeastern Brazil) during the mid-late Holocene and Anthropocene based on sedimentary features, palynological, and geochemical (δ13C, δ15N, C/N) data integrated with spatial-temporal analysis based on satellite images. The data indicated three phases for the mangrove development: (1°) mangrove expansion on tidal flats with estuarine organic matter between >4420 and ~2870 cal yrs BP, under the influence of the mid-Holocene sea-level highstand; (2°) mangrove contraction with an increased contribution of C3 terrestrial plants between ~2870 and ~84 cal yrs BP due to an RSL fall, and (3°) mangrove expansion onto the highest tidal flats since ~84 cal yr BP due to a relative sea-level rise. However, significant mangrove areas were converted to fish farming before 1984 CE. Spatial-temporal analysis also indicated a mangrove expansion since 1984 CE due to mangrove recolonization of shrimp farming areas previously deforested for pisciculture. This work mainly evidenced a trend of mangrove expansion due to RSL rise preceding the effects of anthropogenic emissions of CO2 in the atmosphere and the resilience of these forests in the face of anthropogenic interventions.

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To mitigate floods and storm surges, coastal communities across the globe are under the pressure of high-cost interventions, such as coastal barriers, jetties, and renourishment projects, especially in areas prone to hurricanes and other natural disturbances. To evaluate the effectiveness of these coastal projects in a timely fashion, this methodology is supported by a Geographic Information System that is instaneously fed by regional and local data obtained shortly (24 h) after the disturbance event. Our study assesses the application of 3D models based on aerophotogrammetry from a Phantom 4 RTK drone, following a methodological flowchart with three phases. The Digital Elevation Models (DEMs) based on aerophotogrammetry obtained from a Phantom 4 RTK drone presented a low margin of error (± 5 cm) to dispense Ground Control Points. This technique enables a rapid assessment of inaccessible coastal areas due, for instance, to hurricane impacts. Evaluation of DEMs before and after the disturbance event allows quantifying the magnitudes of shoreline retreat, storm surges, difference in coastal sedimentary volumes, and identifying areas where erosion and sediment accretion occur. Orthomosaics permit the individualization and quantification of changes in vegetation units/geomorphological areas and damages to urban and coastal infrastructure. Our experience monitoring coastal dynamics in North and South America during the last decade indicates that this methodology provides an essential data flow for short and long-term decision-making regarding strategies to mitigate disaster impacts.•Permanent and regional monitoring with spatial-temporal analysis based on satellite/aerial images and lidar data prior to the event.•Local DEMs based on drone aerophotogrammetry after the event.•Integration of regional and local planialtimetric/environmental data.

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Remotely Piloted Aircrafts (RPAs) are commonly used as a platform for collecting images which can be processed with Structure from Motion-Multi View Stereo (SfM-MVS) to generate 3D models. However, mobile applications for mapping planning are not designed for image acquisition of vertical surfaces, such as quarry walls or large cliffs, leaving the user to a manual flight operation, which does not ensure optimal overlap between images. Here we describe a workflow, based on the Litchi App, for automated RPA missions designed to acquire images of vertical surfaces or structures.•An easy-to-follow 8 steps method to survey vertical surfaces using a Remotely Piloted Aircraft.•It can be applied to outcrops, quarry walls, high cliffs and virtually any other type of vertical surface.•The workflow is flexible and can be adapted to a variety of target configurations and user-defined parameters.

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The use of unmanned aerial vehicles (UAVs) in the agricultural and forestry sectors has constantly evolved due to its great versatility and applicability in the field. In this sense, this study provided a statistical overview of studies on the use of UAVs in agricultural and forestry through a bibliometric and scientometric analysis. For that, a research was carried out on the Scopus platform using the terms UAV, UAS, drone, and RPA, together with "agricult*" or "forest* or livestock". Only manuscripts published in English and from January 2000 to December 2020 were selected. The VOSviewer software was used for the analyses. The USA and China were responsible for more than 38% of the publications worldwide. Furthermore, about 50% of the countries in the world showed some scientific record of the use of UAVs in agricultural and forestry studies. The term UAS was more used until 2016, while UAV was more mentioned between 2017 and 2018. Conversely, drone was more endorsed from 2019. The constant increase in scientific production reported in the research and the evolution of the co-occurrence of keywords corroborated two ideas: i) the use of UAVs is still undergoing transformations and is directly related to the advancement of technology included in these equipments; and ii) studies are still not enough to explore all the applicability of the UAVs in agriculture, livestock and forestry.

A utilização de veículos aéreos não tripulados (VANTs) nos setores agrícola e florestal tem evoluído constantemente devido a sua grande versatilidade e aplicabilidade no campo. Nesse sentido, este trabalho teve como objetivo fornecer um panorama estatístico dos estudos sobre o uso de VANTs na agropecuária e silvicultura por meio de uma análise bibliométrica e cientométrica. Para isso, foi realizada uma pesquisa na plataforma Scopus utilizando os termos UAV, UAS, drone e RPA, juntamente com "agricult*" ou "forest* ou livestock". Foram selecionados apenas manuscritos publicados em inglês, de janeiro de 2000 a dezembro de 2020. O software VOSviewer foi utilizado para as análises. Os EUA e a China foram responsáveis por mais de 38% das publicações mundiais. Além disso, cerca de 50% dos países do mundo apresentaram algum registro científico do uso de VANTs em estudos agrícolas e florestais. O termo UAS foi mais utilizado até 2016, enquanto o UAV foi mais mencionado entre 2017 e 2018. Por outro lado, drone foi mais endossado a partir de 2019. O constante aumento da produção científica encontrado nas pesquisas e a evolução da coocorrência de palavras-chave corroboraram duas ideias: i) o uso de VANTs ainda está passando por transformações e está diretamente relacionado ao avanço da tecnologia inserida nesses equipamentos; e ii) os estudos ainda não são suficientes para explorar toda a aplicabilidade dos VANTs na agricultura, pecuária e silvicultura.

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IoT encompasses various objects, technologies, communication standards, sensors, actuators in powered environments, and networked communication. The concept adopted here, IoT off-grid, considers an environment without commercial electricity and commercial internet. Managing various utilities with IoT and collecting the relevant information from this environment is the purpose of this project. It uses machine learning to select relevant data. These data are collected safely using a drone that travels through the off-grid stations. A systematic literature mapping is presented, identifying the state of the art. The result is a software architecture proposal with configurations in the drone and off-grid stations that contemplate data collection from the IoT off-grid environment. The results are also presented with different selection algorithms used in machine learning and final execution in the prototype.

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Industry 4.0 involves various areas of engineering such as advanced robotics, Internet of Things, simulation, and augmented reality, which are focused on the development of smart factories. The present work presents the design and application of the methodology for the development of augmented reality applications (MeDARA) using a concrete, pictorial, and abstract approach with the intention of promoting the knowledge, skills, and attitudes of the students within the conceptual framework of educational mechatronics (EMCF). The flight of a drone is presented as a case study, where the concrete level involves the manipulation of the drone in a simulation; the graphic level requires the elaboration of an experiential storyboard that shows the scenes of the student's interaction with the drone in the concrete level; and finally, the abstract level involves the planning of user stories and acceptance criteria, the computer design of the drone, the mock-ups of the application, the coding in Unity and Android Studio, and its integration to perform unit and acceptance tests. Finally, evidence of the tests is shown to demonstrate the results of the application of the MeDARA.

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The goal of this work is to present a systematic literature mapping (SLM) identifying algorithms for the search for data, determining the best path and types of communication between the local server and the drone, as well as possible simulators to validate proposed solutions. The concept, here considered as IoT Off-Grid, is characterized by being an environment without commercial electrical infrastructure and without communication connected to the internet. IoT equipment generates data to be stored on a local server. It collects these data through a drone that searches each local server for later integration with the commercial internet environment. As a result, we have algorithms to determine the best path based on the TSP-travelling salesman problem. Different types of communication between the drone and the server contain the data, predominantly WiFi 802.11. As a simulator, OMNeT++ stands out.

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The pandemic by COVID-19 is causing a devastating effect on the health of the global population. Currently, there are several efforts to prevent the spread of the virus. Among those efforts, cleaning and disinfecting public areas have become important tasks and they should be automated in future smart cities. To contribute in this direction, this paper proposes a coverage path planning method for a spraying drone, an unmanned aerial vehicle that has mounted a sprayer/sprinkler system, that can disinfect areas. State-of-the-art planners consider a camera instead of a sprinkler, in consequence, the expected coverage will differ in running time because the liquid dispersion is different from a camera's projection model. In addition, current planners assume that the vehicles can fly outside the target region; this assumption can not be satisfied in our problem, because disinfections are performed at low altitudes. Our method presents i) a new sprayer/sprinkler model that fits a more realistic coverage volume to the drop dispersion and ii) a planning method that efficiently restricts the flight to the region of interest avoiding potential collisions in bounded scenes. The algorithm has been tested in several simulation scenes, showing that it is effective and covers more areas with respect to two approaches in the literature. Note that the proposal is not limited to disinfection applications, but can be applied to other ones, such as painting or precision agriculture.

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Beekeepers around the world select bees' characteristics that facilitate and favor production. In regions where hybridization among lineages is taking place, this selection is a challenge, given that these regions are "natural laboratories", where the action of evolutionary processes of a population or species occurs in real time. A natural honeybee (Apis mellifera) hybrid zone exists in Argentina between 28° and 35° South, where Africanized (AHB) and European (EHB) populations converge. In this zone, beekeepers use selected genetic resources of European origin mostly, since the local Africanized bees show a higher defensive behavior, which is not desirable for management. Although EHB colonies have many advantages for honey production, they are not fully adapted to the subtropical climate and are susceptible to certain parasitosis such as varroosis. In addition, both AHB and EHB mate in drone congregation areas (DCAs), where males and virgin queens fly to meet, resulting in variability in the desired characteristics. In this study, we explored the degree of hybridization within a DCA and its reference apiary, located in the province of Entre Ríos, by applying two complementary techniques. First, morphotypes with different degrees of hybridization between European and African subspecies were observed in the reference apiary, indicating a high sensitivity of this morphometric approach to detect hybridization in these populations. Second, a genetic analysis revealed haplotypes of both origins for drones in DCAs, with a higher prevalence of European haplotypes, while all the colonies from the reference apiary exhibited European haplotypes. Overall, our results are in line with the strong impact that commercial beekeeping has on the genetics of DCAs. We show how wing morphometry may be used to monitor hybridization between European and African subspecies, a tool that may be evaluated in other regions of the world where hybridization occurs.

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Recent advances have shown for the first time that it is possible to beat a human with an autonomous drone in a drone race. However, this solution relies heavily on external sensors, specifically on the use of a motion capture system. Thus, a truly autonomous solution demands performing computationally intensive tasks such as gate detection, drone localisation, and state estimation. To this end, other solutions rely on specialised hardware such as graphics processing units (GPUs) whose onboard hardware versions are not as powerful as those available for desktop and server computers. An alternative is to combine specialised hardware with smart sensors capable of processing specific tasks on the chip, alleviating the need for the onboard processor to perform these computations. Motivated by this, we present the initial results of adapting a novel smart camera, known as the OpenCV AI Kit or OAK-D, as part of a solution for the ADR running entirely on board. This smart camera performs neural inference on the chip that does not use a GPU. It can also perform depth estimation with a stereo rig and run neural network models using images from a 4K colour camera as the input. Additionally, seeking to limit the payload to 200 g, we present a new 3D-printed design of the camera's back case, reducing the original weight 40%, thus enabling the drone to carry it in tandem with a host onboard computer, the Intel Stick compute, where we run a controller based on gate detection. The latter is performed with a neural model running on an OAK-D at an operation frequency of 40 Hz, enabling the drone to fly at a speed of 2 m/s. We deem these initial results promising toward the development of a truly autonomous solution that will run intensive computational tasks fully on board.

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This paper presents a fast factorized back-projection (FFBP) algorithm that can satisfactorily process real P-band synthetic aperture radar (SAR) data collected from a spiral flight pattern performed by a drone-borne SAR system. Choosing the best setup when processing SAR data with an FFBP algorithm is not so straightforward, so predicting how this choice will affect the quality of the output image is valuable information. This paper provides a statistical phase error analysis to validate the hypothesis that the phase error standard deviation can be predicted by geometric parameters specified at the start of processing. In particular, for a phase error standard deviation of ~12°, the FFBP is up to 21 times faster than the direct back-projection algorithm for 3D images and up to 13 times faster for 2D images.

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Aedes aegypti control programs require more sensitive tools in order to survey domestic and peridomestic larval habitats for dengue and other arbovirus prevention areas. As a consequence of the COVID-19 pandemic, field technicians have faced a new occupational hazard during their work activities in dengue surveillance and control. Safer strategies to monitor larval populations, in addition to minimum householder contact, are undoubtedly urgently needed. Drones can be part of the solution in urban and rural areas that are dengue-endemic. Throughout this study, the proportion of larvae breeding sites found in the roofs and backyards of houses were assessed using drone images. Concurrently, the traditional ground field technician's surveillance was utilized to sample the same house groups. The results were analyzed in order to compare the effectiveness of both field surveillance approaches. Aerial images of 216 houses from El Vergel village in Tapachula, Chiapas, Mexico, at a height of 30 m, were obtained using a drone. Each household was sampled indoors and outdoors by vector control personnel targeting all the containers that potentially served as Aedes aegypti breeding sites. The main results were that the drone could find 1 container per 2.8 found by ground surveillance; however, containers that were inaccessible by technicians in roofs and backyards, such as plastic buckets and tubs, disposable plastic containers and flowerpots were more often detected by drones than traditional ground surveillance. This new technological approach would undoubtedly improve the surveillance of Aedes aegypti in household environments, and better vector control activities would therefore be achieved in dengue-endemic countries.