Radiometric Compensation Method for NDVI Measurement Using Emulated Targets

The availability, cost, and applicability of Unmanned Aerial Vehicles (UAVs) are rapidly altering the way we perceive the landscape, can access sites, and model the landscape as it unfolds through remote sensing technologies. It falls to present and future researchers to take advantage of this and use the technology to improve our understanding of our surroundings. One way that geomorphologists would be able to take advantage of the improvements in technology is to use the UAV to map soil processes, in particular erosion. The current methods and technology for mapping gully erosion do not look at the smaller features but rather the landscape as a whole. The purpose of this research was to assess the prospects of UAV technology mapping gully erosion. Specifically, the study investigates the application of the UAV imagery to other technologies or methods in mapping gully erosion activities. This was achieved by testing the multiple available modelling software, by mapping objects of known shapes and volumes to determine which software produced results comparable to that of the known shapes. Tests were conducted to determine if this software and UAV combination would have the ability to map and 3D render soil erosion, thus allowing researchers to verify if this technique could be implemented into mapping gully systems. From these tests, it was found the UAV was able to improve the resolution to that of the available methods. Obtaining a resolution of 0.03m would allow geomorphologists to map and situate the soil erosional landscapes and map the test objects. The software package AgiSoft MetaShape had the highest accuracy and reliability in mapping various objects making it ideal in the mapping of smaller erosional features. Multiple erosional landscapes were mapped using a UAV and the attached RGB sensor and a second UAV with a multi-spectral sensor and AgiSoft MetaShape software. Both sensors mapped plant health, capturing plant health with band widths related to each sensor. Ideally the multi-spectral sensors were seen to be more versatile, however the RGB sensor can be useful. The combination of both UAVs created a 3D-rendered model, and the resulting data was useful in determining the potential future areas of erosion and existing areas of high erosion risk. The multi-sensor camera allowed the user to determine micro-features that could potentially become erosional areas in the future as it identified areas with a concentration in water flow. In conclusion, the UAV is a useful spatial tool to monitor, measure and manage soil erosion and gully mapping. Iqoqa. Ukusetshenziswa kweUAV kwenza izinguquko ngokushesha endleleni okubukwa ngayo izwe (landscape) kanti kukubacwaningi ukuthi sithathe ithuba lokusebenzisa lensizakusebenza ngendlela ezoba wusizo ukuze sibe nokuqonda okungcono kolwazi olukhona. Lobuchwepheshe bungasetshenziswa ukubona ukuqopha ukunyakaza komhlabathi ngoguguleka kwemihosha . Inhloso kube wukuhlola ukusetshenziswa kwe UAV ukubona ukuthi iyakwazi yini ukukhiqiza izithombe ezithathwe ngobuchwepheshe basemoyeni obusezingeni eliphezulu. Inhloso yenzekie ngokuthi kwenziwe ukuhlola okunhlobonhlobo kwimodelling software. Lokhu kwenziwe ngezimpawu zebalazwe zomumo owaziwayo kanye nezinga lomsindo, ukuqonda ukuthi ilona luphi uhlelokusebenza olusebenzayo ngezinga lomsindo oseduze . Ukuhlolwa kwenziwe ukubona ukuthi lenhlanganisela yohlelokusebenza ne UAV ingakwazi yini ukuveza ukuguguleka kwenhlabathi igugulwa ngebalalazwe nemodli ye3D kanye nokuqinisekisa ukuthi lobu buchwepheshe bungasetshenziswa ebalazweni lwemihosha kanye nemodeli yekhompyutha eyaziwa nge 3D computer models of gullies. Kulokhu kuhlola kutholakale ukuthi iUAV iyakwazi ukuthuthukisa ukuhleleka kohlelo okukhona. Ukuthola ukuhleleka okungango 0.03 m okungavumela igeomorphologist ukungena ebalazweni kuphinde kuveze ukuguguleka komhlabathi. Izinjongo eziwusayizi owaziwayo zifakwe ebalazweni, okuzale iAgiSoft MetaShape okube sezingeni eliphezulu lokuqagula kanye nokwethembeka ekufakeni ebalazweni izinto ezahlukahlukene . Imiphumela yemininingo ibe wusizo ukuqonda izindawo ezingaguguleka ngengomuso kanye nezindawo ezikhona ezisethubeni lobungozi bokuguguleka .Ikhamera eyimulti sensor yenze umcwaningi ukuthi abone izimpawu ezaziwa ngemicro features ezingaba sethubeni lokuba sendaweni engaguguleka ngengomuso njengoba zikhombisa ukuba nenkinga yobuningi kwamanzi agelezayo. Ukuphetha, i-UAV okwamanje iyinsizakusebenza ewusizo engasetshenziswa ocwaningweni lwegeomorphologists futhi ishibhile kunezinye izinsizakusebenza ezifana nemanned vehicle noma isatellite.

Unmanned Aerial Vehicles (UAVs), can be controlled automatically or with the help of a remote control system, provide high spatial and temporal resolution images in addition to its advantages in terms of cost and time. Due to these features, UAV’s have been widely used in studies such as map production, 3D modeling and volume calculation in recent years. In this study, it is aimed to investigate the effects of different flight parameters on the accuracy of Orthomosaic and DEM produced with UAV Systems. In addition, the rolling shutter effect and the use of pre-calibrated and automatic camera calibration parameters were also tested. For this purpose, a total of seven UAV flights were carried out at different heights, overlap ratios, angles and grids at a mining area located within the borders of Aksaray province. Agisoft MetaShape and Pix4D Mapper, which are widely used commercial software, were used to process the UAV images. In this study, 27 points were established and 3D coordinates of these points were measured using GNSS technique. 15 of these points were used as reference points and the remaining 12 points were used as check points. Accuracy analysis was performed by comparing the produced Orthomosaics and DEMs with the check points in the study area. CloudCompare and ArcGIS software were used in the comparison of the products as a result of the evaluation of the images. It is concluded that the Orthomosaics produced using 450 angle and DEMs with 600 angle has higher accuracy than the others. Point clouds and DEMs’ produced using automatic and pre-calibrated camera calibration parameters show that the pre-calibrated images provide higher accuracy. Also, it is seen that when the rolling shutter effect is modeled, the horizontal and vertical accuracy is increased in all three flights with different flight parameters in this study.

UAV multispectral remote sensing for agriculture: A comparative study of radiometric correction methods under varying illumination conditions

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.

The accelerating impacts of climate change continue to challenge global agriculture, increasing the need for adaptive and sustainable technological solutions. In this context, Unmanned Aerial Vehicles (UAVs) have evolved from experimental tools to key components of precision agriculture, offering advanced capabilities for monitoring, diagnosis, and targeted intervention. This Research Topic explores how UAVs, combined with remote sensing, AI, and operational strategies, can advance climate-smart agriculture. The topic focuses on innovative technologies and UAV applications in precision agriculture for mitigating climate change, highlighting recent advancements.The four contributions in this collection illustrate UAVs' potential to improve resource-use efficiency, enhance plant protection, enable early detection of biotic stress, and support scalable, data-driven decision-making. Together, these studies underline both the versatility of UAV technologies and the need for continued innovation to address environmental variability, operational challenges, and integration with complementary sensing platforms.Regarding UAV for spraying, the: operational variability and deposition efficiency,

In recent years, the utilization of unmanned aerial vehicles (UAVs) has surged in a variety of applications, including weather monitoring, emergency search and rescue operations, and smart agriculture. In these applications, UAVs are instrumental in executing data collection tasks. However, due to the scarcity of resources such as battery capacity, the duration of work is limited, necessitating the optimization of UAV trajectories and resource allocation. In this paper, we introduce a hierarchical task scheduling and resource allocation scheme for UAV data collection tasks, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous actions, including UAV trajectories, UAV communication power, and UAV CPU main frequency, are first optimized in the lower layer, while the upper one concentrates on the discrete action space for the target allocation. In particular, the well-trained lower-layer network is integrated into the upper layer, facilitating rapid global reward feedback between the two-layer network and accelerating the training process of the upper-layer network. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the amount of collected data as well as UAV energy consumption.

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

The advancements made on Unmanned Aerial Vehicles (UAV) related areas such as Cyber-Physical Systems and Embedded Systems made them more suitable for some industry-specific problems. Applications in fields like Precision Agriculture and Aerial Mapping could benefit considerably with the introduction of UAV use cases. However, there are still some obstacles nowadays in UAV related topics like low processing power and storage capacity. To develop robust and complex systems integrating these UAVs first, we need to address these problems. This paper proposes a cloud platform capable of two-way communication with UAVs and further management of multiple UAVs.

Unmanned Aerial Vehicles (UAVs) are becoming essential tools in precision agriculture, enabling real-time monitoring, improved land management, and more efficient use of resources. Among them, Vertical Takeoff and Landing (VTOL) UAVs are ideal for operating in confined and uneven agricultural terrains. However, UAVs equipped with inverted-V empennages suffer from aerodynamic drawbacks, including directional instability and adverse yaw under sideslip conditions. This paper advances the state of the art by optimizing the aerodynamic performance of inverted-V tail configurations through the integration of a dorsal fin. Using Computational Fluid Dynamics (CFD), we assess multiple dorsal fin designs on a UAV platform with a maximum takeoff weight of 10 kg, 2 kg payload, 2-hour endurance, 10 m/s stall speed, and a 5 km² operational range. We analyze key aerodynamic metrics—lift and drag coefficients, lift-to-drag ratio, yaw moment coefficient—as well as flow behavior via pressure contours and vorticity plots. Results confirm that sideslip angles degrade aerodynamic efficiency, but a properly designed dorsal fin, particularly variation 2, significantly reduces adverse yaw at higher angles of attack and sideslip. This modification enhances UAV stability and flight performance, marking a meaningful improvement in VTOL UAV design for agricultural applications.

Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.

Unmanned aerial vehicles (UAVs), when paired with an onboard camera, have proven to be useful tools in many applications, including aerial photography, precision agriculture, and search and rescue operations. Likewise, UAVs capable of physically interacting with the environment have shown great potential to help people perform dangerous, or time-consuming tasks more safely and efficiently than they could on their own. However, due to onboard computation and battery life limitations and complex flight dynamics, using UAVs to physically interact with the environment is still a developing area of research. Considering these limitations, the primary goals of this work are to (1) develop a new UAV platform for aerial manipulation, (2) develop modular hardware and software for the platform to enable specific tasks to be performed autonomously, and (3) develop a visual target tracking method to enable robust performance of autonomous aerial manipulation tasks in unstructured, real-world environments. To that end, the design of the Interacting-BoomCopter UAV (I-BC) is presented here as a new platform for aerial manipulation. With a simple tricopter frame, a single additional actuator for generating horizontal forces, and lightweight, modular end-effectors, the I-BC aims to balance efficiency and functionality in performing aerial manipulation tasks, and is able to perform various tasks such as mounting sensors in hard-to-reach places, and opening small doors or panels. An onboard camera, force and distance sensors, and a powerful single board computer (SBC) enable the I-BC to operate autonomously in unstructured environments, with potential applications in areas such as large-scale infrastructure inspection, industrial inspection and maintenance, and nuclear decontamination efforts.

In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Markers with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.

Precision agriculture (PA) plays an essential role in resource use and crop yields while minimising environmental impact through data‐driven farming techniques. The combination of unmanned aerial vehicles (UAVs), the Internet of Things (IoT) and wireless sensor networks (WSNs) has significantly transformed the current state of farming, enabling decisions based on data, predicting outcomes and precise control. This review presents the current developments, challenges and complementary advantages of these technologies to improve agricultural efficiency and sustainability in a comprehensive manner. The search timeframe of this search is 2019–2025. The analysis of the WSN‐based systems begins with the analysis of sensing technologies, communication protocols (LoRa, Sigfox, Wi‐Fi, Bluetooth, ZigBee, NB‐IoT and RFID), sensor architecture, energy consumption and path‐loss models, which affect the data transmission in an agricultural setting. It highlights the weaknesses of WSN deployment, such as power consumption and coverage. Second, the use of UAVs in crop monitoring, irrigation, pest detection and resource optimisation is reviewed with references to the incorporation of sensing and data analytics algorithms and the challenges associated with UAV use, such as the short flight duration and energy consumption. Third, IoT‐based frameworks are researched in the context of their roles in the PA of real‐time monitoring, automated controls and smart decision‐making. The findings suggest that a network of WSNs, UAVs and the IoT can be used to enhance monitoring quality, data quality and resource utilisation by multiple orders of magnitude. However, such barriers as energy consumption, connectivity differences, complexity of data and costs will be topical. Overall, the WSN‐UAV‐IoT combo is a potentially fruitful direction that could assist PA to take a step forward in terms of productivity, sustainability and environmental friendliness.

Precision agriculture has emerged as a pivotal approach to achieving sustainable food production by integrating advanced technologies such as Unmanned Aerial Vehicles (UAVs), satellite remote sensing, and machine learning. This review examines the synergistic application of these technologies in enhancing agricultural efficiency, resource optimization, and environmental sustainability. UAVs enable high-resolution, real-time monitoring of crop health, soil conditions, and pest infestations, while satellite remote sensing provides scalable, large-scale agricultural data for comprehensive landscape analysis. Machine learning algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs) and Random Forests (RFs), process complex datasets to deliver actionable insights for precision decision-making, such as yield prediction, nutrient management, and irrigation optimization. Case studies demonstrate that integrating UAV and satellite data with machine learning improves crop yield prediction accuracy and resource use efficiency, reducing irrigation costs by 20–25% and nitrogen application by up to 31 kg ha −1 , without compromising productivity. AI-driven disease detection systems have demonstrated high efficacy, with certain models achieving accuracy exceeding 95% in identifying diseases such as Botrytis cinerea in tomatoes, powdery mildew in wheat, and downy mildew in grapes. However, challenges persist, including data processing complexities, high computational demands, and the need for cost-effective, scalable solutions. The findings underscore the transformative potential of these technologies in advancing sustainable agriculture, while emphasizing the necessity for interdisciplinary collaboration, supportive policies such as subsidies for precision agriculture equipment, streamlined regulations for UAV operations, and open data initiatives for satellite imagery, as well as improved accessibility to key technologies including high-resolution multispectral sensors, cloud computing infrastructure, and scalable machine learning platforms for smallholder farmers. This review provides a roadmap for future research and policy development aimed at optimizing food production systems in the face of climate change and growing population demands.