Search Results (45)

An aging population, increased life expectancy and loneliness among older people constitute a growing challenge, driving interest in technological solutions such as home drones. The aim of this study is to analyze their potential for older adults through a systematic review following PRISMA guidelines, including articles indexed in Web of Science, Scopus, PubMed and the ACM Digital Library up to February 2026 and following the Joanna Briggs Institute (JBI) methodology. A total of 285 records were initially identified and imported into JBI, of which 41 duplicate records were removed, and 231 studies were excluded after screening, resulting in 13 studies meeting the inclusion criteria. The reviewed studies suggest generally favorable perceptions among some older adults regarding the use of drones in the areas of health, support and safety, alongside barriers related to usability, trust and user interaction. Recent studies incorporate practical applications, highlighting the potential applicability of drones in supporting aspects related to autonomy, health and safety among older adults. Overall, the literature, though still limited, shows a shift towards more specific applications, highlighting the potential of drones to support the autonomy, health and safety of older adults, although their implementation remains influenced by factors of acceptance and user experience. Full article

White-tailed Deer (Odocoileus virginianus) overabundance has emerged as a significant ecological concern in recent decades. With current populations exceeding 30 million, White-tailed Deer (WTD) are now one of the most spatially abundant ungulate species across both natural and human-altered environments. High densities have led to considerable ecological and economic impacts, including forest understory degradation, biodiversity loss, and increased deer-vehicle collisions. This study examines the spatiotemporal distribution of WTD within three sites at Binghamton University, a heavily wooded campus in the Appalachian Upland region of New York State. To monitor population densities and movement patterns, a combination of remote sensing techniques was employed, including six Assark PH960W trail cameras and a DJI Mavic 3T UAV equipped with an uncooled VOx microbolometer thermal infrared (IR) sensor. Data were collected between 31 October 2024 and 10 March 2025, in relation to three deer culling events on 18 December 2024, 2 January 2025, and 9 January 2025. While Unoccupied Aerial Vehicle (UAV) based thermal imaging proved effective for estimating population dynamics, its utility is constrained by environmental and logistical limitations. In contrast, WiFi-enabled trail cameras provide a cost-efficient approach for capturing high-temporal resolution data at localized sites. Density estimates were derived from UAV thermal imaging and Random Encounter and Staying Time (REST) model calculations, ranging from 13.2 to 26.8 deer/km² across the region. Findings underscore the need for ongoing deer management strategies on campus to support long-term forest ecosystem health. Full article

Illegal dumping poses serious risks to ecosystems and human health, requiring effective and timely monitoring strategies. Advances in uncrewed aerial vehicles (UAVs), photogrammetry, and deep learning (DL) have created new opportunities for detecting and characterizing waste objects over large areas. Within the framework of the EMERITUS Project, an EU Horizon Europe initiative supporting the fight against environmental crimes, this study evaluates the performance of pre-trained prompt-based multimodal (PBM) DL models integrated into ArcGIS Pro for object detection and segmentation. To test such models, UAV surveys were specially conducted at a semi-controlled test site in northern Italy, producing very high-resolution orthoimages and video frames populated with simulated waste objects such as tyres, barrels, and sand piles. Three PBM models (CLIPSeg, GroundingDINO, and TextSAM) were tested under varying hyperparameters and input conditions, including orthophotos at multiple resolutions and frames extracted from UAV-acquired videos. Results show that model performance is highly dependent on object type and imagery resolution. In contrast, within the limited ranges tested, hyperparameter tuning rarely produced significant improvements. The evaluation of the models was performed using low IoU to generalize across different types of detection models and to focus on the ability of detecting object. When evaluating the models with orthoimagery, CLIPSeg achieved the highest accuracy with F1 scores up to 0.88 for tyres, whereas barrels and ambiguous classes consistently underperformed. Video-derived (oblique) frames generally outperformed orthophotos, reflecting a closer match to model training perspectives. Despite the current limitations in performances highlighted by the tests, PBM models demonstrate strong potential for democratizing GeoAI (Geospatial Artificial Intelligence). These tools effectively enable non-expert users to employ zero-shot classification in UAV-based monitoring workflows targeting environmental crime. Full article

Globally, sea turtles face significant threats from human activities, yet detailed information on their habitat use and specific anthropogenic impacts remains limited, particularly in key marine protected areas like Kenya’s Diani-Chale National Marine Reserve (DCNMR). This study utilized drone-based (UAV—unmanned aerial vehicle) monitoring and geospatial analysis to assess sea turtle distribution and habitat use, integrating data from the Allen Coral Atlas. Most sea turtle sightings occurred in reef zones (61.86%), while the reef slope was the most utilized geomorphic feature (26.7% of sightings). The study identified a significant sea turtle hotspot in the northern DCNMR, a region characterized by lower anthropogenic pressure and unique geomorphic features. Between February and July 2024, we conducted monthly UAV surveys (6–10 survey days per month) in the DDCNMR using a DJI Mavic 3 drone, completing multiple standardized 25-min flights per day that each covered ~1 km² via non-overlapping transects at 30–40 m altitude under optimal sea state and visibility conditions, resulting in 233 sea turtle sightings. UAV survey data were summarized descriptively, with sea turtle sightings mapped against geomorphological features as well as benthic habitats from an open source, high-resolution, satellite-based map and monitoring system for shallow-water coral reefs (ACA—Allen Coral Atlas). Allen Coral Atlas data and drone observations indicate that a widened reef slope and estuarine nutrient inputs provide a critical habitat gradient, offering turtles tidal-independent access to shallow foraging flats. Based on these findings, we recommend designating the northern reef slope as a priority no-take zone and conducting seagrass health assessments to guide potential restoration. This research demonstrates the utility of integrating drone surveys with open access geospatial tools to provide the actionable spatial data necessary for targeted sea turtle conservation and informed marine spatial planning. Full article

Food shortages are becoming increasingly urgent due to the growing global population. Enhancing oil crop yields, particularly sunflowers, is key to ensuring food security and the sustainable provision of vegetable fats essential for human nutrition and animal feed. However, sunflower yields are often reduced by diseases, pests, and other factors. Remote sensing technologies, such as unmanned aerial vehicle (UAV) scans and satellite monitoring, combined with machine learning algorithms, provide powerful tools for monitoring crop health, diagnosing diseases, mapping fields, and forecasting yields. These technologies enhance agricultural efficiency and reduce environmental impact, supporting sustainable development in agriculture. This systematic review aims to assess the accuracy of various machine learning technologies, including classification and segmentation algorithms, convolutional neural networks, random forests, and support vector machines. These methods are applied to monitor sunflower crop conditions, diagnose diseases, and forecast yields. It provides a comprehensive analysis of current methods and their potential for precision farming applications. The review also discusses future research directions, including the development of automated systems for crop monitoring and disease diagnostics. Full article

Severe earthquakes, extreme floods, tragic accidents, mega-fires, and even viruses belong to disasters that can destroy the economic, social, or cultural life of people. Due to the climate crisis, disasters will likely become more frequent and intense over the years. Unmanned aerial vehicles (UAVs/drones) have obtained an increasing role in disaster management, which was particularly evident during the COVID-19 pandemic. However, lack of social acceptability remains a limiting factor of drone usage. Drones as a means of state surveillance—possibly mass surveillance—are subject to certain limits since their advanced monitoring technology, including Artificial Intelligence, may affect human rights, such as the right to privacy. Due to the severity of the pandemic, which has been described as the “ideal state of emergency”, despite the rising use of drones, such privacy concerns have been underestimated so far. At the same time, the existing approach of the European Court of Human Rights (ECtHR) and the Court of Justice of the European Union (CJEU) regarding the COVID-19 health crisis and human rights during emergencies seems rather conservative and, thus, setting limits between conflicting rights in such exceptional circumstances remains vague. Under these conditions, the fear that the COVID-19 pandemic may have become a starting point for transitioning to a world normalizing the exception is evident. Such fear in terms of privacy implies a world with a narrowed scope of privacy; thus, setting questions and exploring the challenges about the future of drone regulation, especially in the European Union, are crucial. Full article

Grasslands play a crucial role in ecosystems, influencing key ecological functions such as biodiversity, climate regulation, and soil and water conservation. With the impacts of environmental changes and human activities, the functional status and health of grasslands are facing challenges. Therefore, efficient identification of grassland status is of great significance for the sustainable management, ecological protection, and restoration of grassland resources. To support the sustainable development of grasslands, the GRCNet network model is proposed. Grassland sweep photography was performed via a UAV-mounted hyperspectral imager to establish a 13-category grassland hyperspectral dataset. Then, Gaussian filter and principal component analysis (PCA) were used for noise reduction and dimensionality reduction in the hyperspectral images, and the GRCNet network model, which mainly consists of the GCA module, the RDC module, and the VIT-Base module, was established for the classification task. The experiments use the average accuracy, overall accuracy, F1 score, Kappa coefficient, and running time as the performance indexes, and the eight methods are compared with GRCNet. The results showed that the GRCNet network performed the best with AA reaching 92.84%, OA reaching 94.59%, F1 score reaching 97.22, and Kappa coefficient reaching 0.93. The GRCNet method is 10–20% more accurate than other methods. Comparative tests were also conducted using two public datasets, and GRCNet performed the best with a 2–20% improvement in accuracy. The results demonstrate the effectiveness of the GRCNet network model in hyperspectral grass classification tasks, which can be used as an efficient solution to the current problem of low hyperspectral accuracy and poor stability. Full article

Sustainable forestry for the management of forest resources is more important today than ever before because keeping forests healthy has an impact on human health. Recent advances in Unmanned Aerial Vehicles (UAVs), computer vision, and Deep Learning (DL) models make remote sensing for Forest Insect Pest and Disease (FIPD) possible. In this work, a UAV-based remote sensing process, computer vision, and a Deep Learning framework are used to automatically and efficiently detect and map areas damaged by bark beetles in a Mexican forest located in the Hidalgo State. First, the image dataset of the region of interest (ROI) is acquired by a UAV open hardware platform. To determine healthy trees, we use the tree crown detection prebuilt Deepforest model, and the trees diseased by pests are recognized using YOLOv5. To map the area of the damaged region, we propose a method based on morphological image operations. The system generates a comprehensive report detailing the location of affected zones, the total area of the damaged regions, GPS co-ordinates, and both healthy and damaged tree locations. The overall accuracy rates were 88% and 90%, respectively. The results obtained from a total area of 8.2743 ha revealed that 16.8% of the surface was affected and, of the 455 trees evaluated, 34.95% were damaged. These findings provide evidence of a fast and reliable tool for the early evaluation of bark beetle impact, which could be expanded to other tree and insect species. Full article

Marine and freshwater plastic pollution is a worldwide problem affecting ecosystems and human health. Although remote sensing has been used to map large floating plastic rafts, there are research gaps in detecting submerged plastic due to the limited amount of in situ data. This study is the first to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of the plastic signal. A large 10 × 10 m artificial polymer tarpaulin was deployed in a freshwater lake for a two-week period and was captured by a multi-sensor and multi-resolution unmanned aerial vehicle (UAV) and satellite. Spectral analysis was conducted to assess the attenuation of individual wavelengths of the submerged tarpaulin in UAV hyperspectral and Sentinel-2 multispectral data. A K-Means unsupervised clustering algorithm was used to classify the images into two clusters: plastic and water. Additionally, we estimated the optimal number of clusters present in the hyperspectral dataset and found that classifying the image into four classes (water, submerged plastic, near surface plastic and buoys) significantly improved the accuracy of the K-Means predictions. The submerged plastic tarpaulin was detectable to ~0.5 m below the water surface in near infrared (NIR) (~810 nm) and red edge (~730 nm) wavelengths. However, the red spectrum (~669 nm) performed the best with ~84% true plastic positives, classifying plastic pixels correctly even to ~1 m depth. These individual bands outperformed the dedicated Plastic Index (PI) derived from the UAV dataset. Additionally, this study showed that in neither Sentinel-2 bands, nor the derived indices (PI or Floating Debris Index (FDI), it is currently possible to determine if and how much of the tarpaulin was under the water surface, using a plastic tarpaulin object of 10 × 10 m. Overall, this paper showed that spatial resolution was more important than spectral resolution in detecting submerged tarpaulin. These findings directly contributed to Sustainable Development Goal 14.1 on mapping large marine plastic patches of 10 × 10 m and could be used to better define systems for monitoring submerged and floating plastic pollution. Full article

Rice straw is used as livestock feed and compost. Ferimzone and tricyclazole, common fungicides for rice blast control, can be found in high concentrations in rice straw after unmanned aerial vehicle (UAV) spraying, potentially affecting livestock and human health through pesticide residues. In this study, an optimized method for the analysis of the two fungicides in rice straw was developed using the improved QuEChERS method. After the optimization of water and solvent volume, extraction conditions including ethyl acetate (EtOAc), acetonitrile (MeCN), a mixed solvent, and MeCN containing 1% acetic acid were compared. Different salts, including unbuffered sodium chloride, citrate, and acetate buffer salts, were compared for partitioning. Among the preparation methods, the MeCN/EtOAc mixture with unbuffered salts showed the highest recovery rates (88.1–97.9%, RSD ≤ 5.1%). To address the severe matrix effect (%ME) of rice straw, which is characterized by low moisture content and cellulose-based complex matrices, samples were purified using 25 mg each of primary–secondary amine (PSA) and octadecylsilane (C₁₈), without pesticide loss. The developed method was validated with a limit of quantification (LOQ) of 0.005 mg/kg for target pesticides, and recovery rates at levels of 0.01, 0.1, and 2 mg/kg met the permissible range (82.3–98.9%, RSD ≤ 8.3%). The %ME ranged from −17.6% to −0.3%, indicating a negligible effect. This optimized method was subsequently applied to residue studies following multi-rotor spraying. Fungicides from all fields and treatment groups during harvest season did not exceed the maximum residue limits (MRLs) for livestock feed. This confirms that UAV spraying can be safely managed without causing excessive residues. Full article

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments. Full article

River and lake health assessment (RLHA) is an important approach to alleviating the conflict between protecting river and lake ecosystems and fostering socioeconomic development, aiming for comprehensive protection, governance, and management. Vegetation, a key component of the riparian zone, supports and maintains river and lake health (RLH) by providing a range of ecological functions. While research on riparian zone vegetation is ongoing, these studies have not yet been synthesized from the perspective of integrating RLHA with the ecological functions of riparian zone vegetation. In this paper, based on the bibliometric method, the relevant literature studies on the topics of RLHA and unmanned aerial vehicle (UAV) remote sensing of vegetation were screened and counted, and the keywords were highlighted, respectively. Based on the connotation of RLH, this paper categorizes the indicators of RLHA into five aspects: water space: the critical area from the river and lake water body to the land in the riparian zone; water resources: the amount of water in the river and lake; water environment: the quality of water in the river and lake; water ecology:aquatic organisms in the river and lake; and water services:the function of ecosystem services in the river and lake. Based on these five aspects, this paper analyzes the key role of riparian zone vegetation in RLHA. In this paper, the key roles of riparian zone vegetation in RLHA are summarized as follows: stabilizing riverbanks, purifying water quality, regulating water temperature, providing food, replenishing groundwater, providing biological habitats, and beautifying human habitats. This paper analyzes the application of riparian zone vegetation ecological functions in RLH, summarizing the correlation between RLHA indicators and these ecological functions. Moreover, this paper analyzes the advantages of UAV remote sensing technology in the quantitative monitoring of riparian zone vegetation. This analysis is based on the high spatial and temporal resolution characteristics of UAV remote sensing technology and focuses on monitoring the ecological functions of riparian zone vegetation. On this basis, this paper summarizes the content and indicators of UAV quantitative remote sensing monitoring of riparian zone vegetation for RLHA. It covers several aspects: delineation of riparian zone extent, identification of vegetation types and distribution, the influence of vegetation on changes in the river floodplain, vegetation cover, plant diversity, and the impact of vegetation distribution on biological habitat. This paper summarizes the monitoring objects involved in monitoring riparian zones, riparian zone vegetation, river floodplains, and biological habitats, and summarizes the monitoring indicators for each category. Finally, this paper analyzes the challenges of UAV quantitative remote sensing for riparian zone vegetation at the current stage, including the limitations of UAV platforms and sensors, and the complexity of UAV remote sensing data information. This paper envisages the future application prospects of UAV quantitative remote sensing for riparian zone vegetation, including the development of hardware and software such as UAV platforms, sensors, and data technologies, as well as the development of integrated air-to-ground monitoring systems and the construction of UAV quantitative remote sensing platforms tailored to actual management applications. Full article

Marine litter poses a significant global threat to marine ecosystems, primarily driven by poor waste management, inadequate infrastructure, and irresponsible human activities. This research investigates the application of image preprocessing techniques and deep learning algorithms for the detection of seafloor objects, specifically marine debris, using unmanned aerial vehicles (UAVs). The primary objective is to develop non-invasive methods for detecting marine litter to mitigate environmental impacts and support the health of marine ecosystems. Data was collected remotely via UAVs, resulting in a novel database of over 5000 images and 12,000 objects categorized into 31 classes, with metadata such as GPS location, wind speed, and solar parameters. Various image preprocessing methods were employed to enhance underwater object detection, with the Removal of Water Scattering (RoWS) method demonstrating superior performance. The proposed deep neural network architecture significantly improved detection precision compared to existing models. The findings indicate that appropriate databases and preprocessing methods substantially enhance the accuracy and precision of underwater object detection algorithms. Full article

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. Full article

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments. Full article