Search Results (121)

Conventional methods of nano-urea application in maize cultivation, such as tractor-operated gun sprayers, involve high water usage, labor intensity, and operator health risks due to chemical exposure. The drone spraying system ensures precise and automated application of nano-urea with minimal resource use, labor requirement, and operator intervention. However, the efficacy of the drone spraying system for nano-urea application was not evaluated and compared with traditional spraying systems in field conditions. There is a need to evaluate whether drone-based spraying systems can provide an equally effective and more resource-efficient alternative to conventional spraying techniques. Therefore, this study evaluated the agronomic efficacy of a drone-based spraying platform in comparison to conventional tractor-operated gun sprayers for the foliar spray application of nano-urea in the maize crop. Field experiments were conducted during the 2024 Kharif season to evaluate changes in SPAD, NDVI values, and grain yield due to two spray application methods. Both spraying methods showed statistically similar NDVI and SPAD values eight days after nano-urea application, indicating comparable effectiveness in nutrient delivery. Maize yield was also observed to be statistically indistinguishable between the two methods (t (8) = 0.025503, p = 0.9803), with 2912 ± 375 kg/ha (mean ± SE) for the gun sprayer and 2928 ± 503 kg/ha for the drone sprayer treatments. However, the drone system demonstrated significant operational advantages, including 95% water savings and decreased operational time. These findings support the use of drone spraying as a sustainable, safe, and scalable alternative to traditional fertilization application practices in precision agriculture. Full article

Modern electronic devices, such as smartphones and drones, integrate various sensors to enable diverse sensor-based applications. Yet, sensor measurements exhibit significant variations across different device models, even under the same environment. These variations arise from sensor biases, which occur in three different types: offset bias (additive constant errors), scale bias (multiplicative proportional errors), and drift bias (time-dependent or temperature-dependent errors). Among the biases, in this paper we specifically target offset bias, which has the greatest impact in typical smartphone usage scenarios. This generally leads to performance degradation in sensor-based applications across various device models and instances. To understand the characteristics of the offset bias, we categorize sensors into sensors with and without absolute reference values. Sensors with absolute references enable direct calibration using theoretical true values, while sensors with relative references require different approaches depending on how sensor applications process the data. For scalar-based applications that determine the current state by comparing a sensor measurement against a pre-defined reference, the offset biases can be removed by the existing procedures using reference devices. However, for sequence-based applications that determine the current state by analyzing relative changes in a sequence, the offset bias issue has not been addressed yet. We propose initial value removal and mean removal algorithms that statically and dynamically remove the offset biases from the sensor data sequences for these sequence-based applications. We evaluate our bias normalization algorithms for two different use cases in a geomagnetic-based indoor positioning system (IPS). First, we evaluate the impact of our bias normalization algorithms on the positioning performance of our LSTM-based IPS. Without bias normalization, although the reference device (Galaxy S23 Plus) showed an average positioning error of 0.6 m, the other three smartphone models (Galaxy S22 Plus, iPhone 15, and iPhone 16 Pro) exhibited much worse positioning performance, with errors of 2.48 m, 18.21 m, and 13.13 m. However, after applying our bias normalization, the average positioning errors of all models dropped below 0.68 m. Second, we also evaluate the impact of the bias normalization on detecting whether the position of a smartphone is in a pocket or in a hand-held state. For this, we analyze the sequence of light sensor measurements. We improved the detection accuracy from 42.3% to 97.6% with bias normalization across all device models without requiring individual threshold settings. Full article

Against the backdrop of the low-altitude economy, with the widespread application of drones in sports scenarios, the driving mechanism of users’ long-term usage intention has become a key issue in technology adoption research. To investigate the critical factors influencing the continuous use of drone products by sports-involved populations, this study builds a factor model for users’ continuous use of drones. It is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, integrating the UTAUT2 model and specific user needs in sports scenarios. Both traditional structural equation modeling (SEM) and Bayesian structural equation modeling (BSEM) are employed for empirical testing. Through the analysis of 297 valid questionnaire responses, it is found that the Bayesian approach yields a better fit. Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Safety and Environmental Compatibility all exert significant positive impacts on users’ continuous usage intention, with Effort Expectancy having the most prominent influence. On this basis, service strategies for drone brands are proposed to support product design and service provision. This study preliminarily indicates that Bayesian analysis possesses advantages and potential in this field. Meanwhile, the factor model for users’ long-term drone usage can meet the development needs in sports scenarios, and it has strong feasibility as a design model for users’ long-term drone usage. Full article

The present paper deals with the combined application of near-surface geophysical techniques in a sustainable agriculture project. Their application is focused on the identification of any subsurface water in the context of sustainable water management for the selected living hub, located in the semi-arid area of Agios Georgios-Mandra Attiki. The objective of the multidisciplinary geophysical study was to determine the depth of the bedrock and the thickness of the post-Alpine deposits. In addition, the subsurface karstification and the possible aquifer presence were examined. For that reason, the following techniques were implemented: Electrical Resistivity Tomography, Seismic Refraction Tomography, Ground-Penetrating Radar, and Very-Low Frequency electromagnetic technique. The study was also supported by drone LiDAR usage. The investigation revealed several hydrogeological characteristics of the area. The thickness of the post-Alpine sediments is almost 3 m. However, no shallow aquiferous systems have been developed in this formation, as indicated by their relatively high resistivity values (100–1000 Ohm.m). Furthermore, the alpine bedrock exhibits extensive karstification, facilitated by the development of fracture zones. The absence of an underlying impermeable layer prevented the development of aquiferous zones, at least up to a depth of 100 m. Full article

This study presented a comprehensive investigation into the performance of object detection models tailored for edge devices, particularly in the context of Unmanned Aerial Vehicle swarms. Object detection plays a pivotal role in enhancing autonomous navigation, situational awareness, and target tracking capabilities within UAV swarms, where computing resources are constrained by the onboard low-cost computers. Initially, a thorough review of the existing literature was conducted to identify state-of-the-art object detection models suitable for deployment on edge devices. These models are evaluated based on their speed, accuracy, and efficiency, with a focus on real-time inference capabilities crucial for Unmanned Aerial Vehicle applications. Following the literature review, selected models undergo empirical validation through custom training using the Vision Meets Drone dataset, a widely recognized dataset for Unmanned Aerial Vehicle-based object detection tasks. Performance metrics such as mean average precision, inference speed, and resource utilization were measured and compared across different models. Lastly, the study extended its analysis beyond traditional object detection to explore the efficacy of instance segmentation and proposed an optimization to an object tracking technique within the context of unmanned Aerial Vehicles. Instance segmentation offers finer-grained object delineation, enabling more precise target or landmark identification and tracking, albeit at higher resource usage and higher inference time. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

The continuous and reliable operation of autonomous systems is important for farm management decision making, whether such systems perform crop monitoring using imaging systems or crop handling in pruning and harvesting applications using robotic manipulators. Autonomous systems, including robotic ground vehicles, drones, and tractors, are major research efforts of precision crop management. However, these systems may be less effective or require specific customizations for planting systems in low tunnels, high tunnels, or other environmentally controlled enclosures. In this work, a compact and lightweight crop management platform is developed that uses overhead power cables for continuous operation over row crops, requiring less human intervention and independent of the ground terrain conditions. The platform does not carry batteries onboard for its operation, but rather pulls power from overhead cables, which it also uses to navigate over crop rows. It is developed to be modular, with the top section consisting of mobility and power delivery and the bottom section addressing a custom task, such as incorporating additional sensors for crop monitoring or manipulators for crop handling. This prototype illustrates the infrastructure, locomotive mechanism, and sample usage of the system (crop imaging) in the application of low-tunnel-covered strawberries; however, there is potential for other row crop systems with regularly spaced support structures to adopt this platform as well. Full article

Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. The discussion extends to their performance in various operational contexts, including stationary, moving, and three-dimensional settings. Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. Furthermore, strategies focused on reducing energy usage and enabling coordinated operations among multiple drones are analyzed, addressing issues such as prolonged operation, distribution of assignments, and navigation around obstacles. Although notable advancements have been achieved, challenges like high computational demands and the need for immediate responsiveness persist. By consolidating the latest progress, this survey provides meaningful perspectives and guidance for the ongoing evolution of UAV route planning solutions. Full article

The rapid deployment of the Internet of Drones (IoD) across different fields has brought forth enormous security threats in real-time data communication. To overcome authentication vulnerabilities, this paper introduces a secure lightweight framework integrating deep learning-based user behavior analysis and cryptographic protocols. The proposed framework is verified through AVISPA security verification against replay, man-in-the-middle, and impersonation attacks. Performance analysis via NS2 simulations based on changing network parameters (5–50 drones, 1–20 users, 2–8 ground stations) validates enhancements in computation overhead, authentication delay, memory usage, power consumption, and communication effectiveness in comparison with recent models such as LDAP, TAUROT, IoD-Auth, and LEMAP, thereby establishing our system as an optimal choice for safe IoD operation. 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

Simultaneous localization and mapping (SLAM) is crucial for autonomous driving, drone navigation, and robot localization, relying on efficient point cloud registration and loop closure detection. Traditional Normal Distributions Transform (NDT) odometry frameworks provide robust solutions but struggle with real-time performance due to the high computational complexity of processing large-scale point clouds. This paper introduces an improved NDT-based LiDAR odometry framework to address these challenges. The proposed method enhances computational efficiency and registration accuracy by introducing a unified feature point cloud framework that integrates planar and edge features, enabling more accurate and efficient inter-frame matching. To further improve loop closure detection, a parallel hybrid approach combining Radius Search and Scan Context is developed, which significantly enhances robustness and accuracy. Additionally, feature-based point cloud registration is seamlessly integrated with full cloud mapping in global optimization, ensuring high-precision pose estimation and detailed environmental reconstruction. Experiments on both public datasets and real-world environments validate the effectiveness of the proposed framework. Compared with traditional NDT, our method achieves trajectory estimation accuracy increases of 35.59% and over 35%, respectively, with and without loop detection. The average registration time is reduced by 66.7%, memory usage is decreased by 23.16%, and CPU usage drops by 19.25%. These results surpass those of existing SLAM systems, such as LOAM. The proposed method demonstrates superior robustness, enabling reliable pose estimation and map construction in dynamic, complex settings. Full article

Intelligent precision agriculture incorporates a number of Internet of Things (IoT) devices and drones to supervise agricultural activities and surroundings. The collected data are then forwarded to processing centers to facilitate crucial decisions. This can potentially help optimize the usage of agricultural resources and thwart disasters, enhancing productivity and profitability. To facilitate monitoring and decision, the smart devices in precision agriculture must exchange massive amounts of data across the open wireless communication channels. This inadvertently introduces a number of vulnerabilities, exposing the collected data to numerous security and privacy threats. To address these issues, massive security solutions have been introduced to secure the communication process in precision agriculture. However, most of the current security solutions either fail to offer perfect protection or are inefficient. In this paper, a scheme deploying efficient cryptographic primitives such as hashing, exclusive OR and random number generators is presented. We utilize the Burrows–Abadi–Needham (BAN) logic to demonstrate the verifiable security of the negotiated session keys. In addition, we execute an extensive semantic analysis which reveals the robustness of our scheme against a myriad of threats. Moreover, comparative performance evaluations demonstrate its computation overheads and energy consumption efficiency. Full article

Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly vital in agriculture and disaster management due to their autonomous monitoring, data collection, and service delivery capability. However, energy constraints often limit their potential, highlighting the need for efficient recharging and energy management solutions. This systematic literature review (SLR) examines the current simulations of drone recharging technologies within precision agriculture and disaster relief. It highlights recent advancements, including various algorithms for path and mission planning, while identifying ongoing challenges, particularly the scarcity of studies on the recharging coordination that affects UAV operations in these fields. The review encompasses 36 high-quality studies from 2038 papers initially found in the literature. Despite significant progress in recharging technologies, achieving sustainable and continuous UAV operation remains challenging, especially in high-demand energy environments such as disaster zones and agricultural areas. We identify three research gaps—knowledge, methodological, and practical. There is a lack of drone recharging studies, as drones are energy-demanding devices. The studies show that the coordination process relies on communication, which can use more battery, and we also find a lack of real-world applications in the studies. Another finding is that the context of disaster is studied more than agricultural usage. Full article

The 20th Party Congress initiated efforts to strengthen maritime power and advance marine ecological civilization, which is essential for promoting sustainable development. To achieve this goal, this study combines field measurements, drone imagery, and high-resolution remote sensing data, using GIS technology to analyze changes in marine resources in Hangzhou Bay and assess marine area usage, intertidal zone area changes, and coastline erosion. The key findings show that the industrial sector accounts for the largest usage of marine area, with the industrial sea area growing by 110.3% from 2018 to 2020. The diversity index for marine area usage in Hangzhou Bay has remained stable, consistently at 0.6 and above over the past five years. The continental coastline of Hangzhou Bay has shown a decreasing trend in recent years from 2018 and 2021, with a total intertidal area of Hangzhou Bay decreased by 73.44 km², where the overall shoal pattern in Hangzhou Bay remained relatively stable from 2008 to 2016. Erosion has been the predominant force, with maximum erosion surpassing 3 m and causing significant spatial changes. Between 2012 and 2016, the total erosion volume reached 192,473.74 × 10⁶ m³, with an average annual erosion rate of 48,118.44 × 10⁶ m³. This process has led to a gradual reduction in the size of affected areas over the period from 2001 to 2021. This research provides valuable insights for authorities to make informed decisions regarding the management of marine spatial resources in Hangzhou Bay. Full article

Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the necessity of this technology is increasing, given its numerous benefits. However, as with any modern and automated technology, security concerns arise from various aspects. In this paper, we discuss cyberthreats to drones, as this technology is vulnerable to attackers during data collection, storage, and usage. Although various techniques and methods have been developed to address attacks on drones, this field remains in its infancy in many respects. This paper provides a comprehensive review of the security challenges associated with the use of agricultural drones. The security issues were thoroughly analyzed, with a particular focus on cybersecurity, categorized into four distinct levels: emerging threats, sensor vulnerabilities, hardware and software attacks, and communication-related threats. Additionally, we examined the limitations and challenges posed by cyberthreats to drone systems. Full article