eProbe: Sampling of Environmental DNA within Tree Canopies with Drones.

Environmental DNA (eDNA) analysis is a powerful tool for studying biodiversity in forests and tree canopies. However, collecting representative eDNA samples from these high and complex environments remains challenging. Traditional methods, such as surface swabbing or tree rolling, are labor-intensive and require significant effort to achieve adequate coverage. This study proposes a novel approach for unmanned aerial vehicles (UAVs) to collect eDNA within tree canopies by using a surface swabbing technique. The method involves lowering a probe from a hovering UAV into the canopy and collecting eDNA as it descends and ascends through branches and leaves. To achieve this, a custom-designed robotic system was developed featuring a winch and a probe for eDNA collection. The design of the probe was optimized, and a control logic for the winch was developed to reduce the risk of entanglement while ensuring sufficient interaction force to facilitate transfer of eDNA onto the probe. The effectiveness of this method was demonstrated during the XPRIZE Rainforest Semi-Finals as 10 eDNA samples were collected from the rainforest canopy, and a total of 152 molecular operational taxonomic units (MOTUs) were identified using eDNA metabarcoding. We further investigate how the number of probe interactions with vegetation, the penetration depth, and the sampling duration influence the DNA concentration and community composition of the samples.

Combinations of First Responder and Drone Delivery to Achieve 5-Minute AED Deployment in OHCA.

Background: Defibrillation in the critical first minutes of out-of-hospital cardiac arrest (OHCA) can significantly improve survival. However, timely access to automated external defibrillators (AEDs) remains a barrier. Objectives: The authors estimated the impact of a statewide program for drone-delivered AEDs in North Carolina integrated into emergency medical service and first responder (FR) response for OHCA. Methods: Using Cardiac Arrest Registry to Enhance Survival registry data, we included 28,292 OHCA patients ≥18 years of age between 1 January 2013 and 31 December 2019 in 48 North Carolina counties. We estimated the improvement in response times (time from 9-1-1 call to AED arrival) achieved by 2 sequential interventions: 1) AEDs for all FRs; and 2) optimized placement of drones to maximize 5-minute AED arrival within each county. Interventions were evaluated with logistic regression models to estimate changes in initial shockable rhythm and survival. Results: Historical county-level median response times were 8.0 minutes (IQR: 7.0-9.0 minutes) with 16.5% of OHCAs having AED arrival times of <5 minutes (IQR: 11.2%-24.3%). Providing all FRs with AEDs improved median response to 7.0 minutes (IQR: 6.2-7.8 minutes) and increased OHCAs with <5-minute AED arrival to 22.3% (IQR: 16.4%-30.9%). Further incorporating optimized drone networks (326 drones across all 48 counties) improved median response to 4.8 minutes (IQR: 4.3-5.2 minutes) and OHCAs with <5-minute AED arrival to 56.3% (IQR: 46.9%-64.2%). Survival rates were estimated to increase by 34% for witnessed OHCAs with estimated drone arrival <5 minutes and ahead of FR and emergency medical service. Conclusions: Deployment of AEDs by FRs and optimized drone delivery can improve AED arrival times which may lead to improved clinical outcomes. Implementation studies are needed.

The effects of drone transportation on routine laboratory, immunohematology, flow cytometry and molecular analyses.

OBJECTIVES: Transportation of medical samples between laboratories or hospital sites is typically performed by motorized ground transport. Due to the increased traffic congestions in urban environments, drone transportation has become an attractive alternative for fast shipping of samples. In accordance with the CLSI guidelines and the ISO 15189 standard, the impact of this transportation type on sample integrity and performance of laboratory tests must be thoroughly validated. METHODS: Blood samples from 36 healthy volunteers and bacterial spiked urine samples were subjected to a 20-40 min drone flight before they were analyzed and compared with their counterparts that stayed on the ground. Effects on stability of 30 routine biochemical and hematological parameters, immunohematology tests and flow cytometry and molecular tests were evaluated. RESULTS: No clinically relevant effects on blood group typing, flow cytometry lymphocyte subset testing and on the stability of the multicopy opacity-associated proteins (Opa) genes in bacterial DNA nor on the number of Abelson murine leukemia viral oncogene homolog 1 (abl) housekeeping genes in human peripheral blood cells were seen. For three of the 30 biochemistry and hematology parameters a statistically significant difference was found: gamma-glutamyl transferase (gamma-GT), mean corpuscular hemoglobin (MCH) and thrombocyte count. A clinically relevant effect however was only seen for potassium and lactate dehydrogenase (LDH). CONCLUSIONS: Multi-rotor drone transportation can be used for medical sample transportation with no effect on the majority of the tested parameters, including flow cytometry and molecular analyses, with the exception of a limited clinical impact on potassium and LDH.

Flight Attitude Estimation with Radar for Remote Sensing Applications.

Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor.

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

INTRODUCTION: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. METHODS: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. RESULTS: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. CONCLUSION: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

Concepts for drone based pipeline leak detection.

The quickly developing drone technology can be used efficiently in the field of pipeline leak detection. The aim of this article is to provide drone mission concepts for detecting releases from pipelines. It provides an overview of the current applications of natural gas pipeline surveys, it considers environmental conditions by plume modelling, it discusses suitable commercially available sensors, and develops concepts for routine monitoring of pipelines and short term missions for localising and identifying a known leakage. Suitable platforms depend on the particular mission and requirements concerning sensors and legislation. As an illustration, a feasibility study during a release experiment is introduced. The main challenge of this study was the variability of wind direction on a time scale of minutes, which produces considerable differences to the plume simulations. Nevertheless, the leakage rates derived from the observations are in the same order of magnitude as the emission rates. Finally the results from the modeling, the release experiment and possible drone scenarios are combined and requirements for future application derived.

Algal Biodiversity of Nine Megaliths in South-East Bulgaria.

This paper presents the first data on the biodiversity of lithophytic algae from Bulgarian megaliths obtained after the application of the direct sampling method, subsequent cultivation, and processing by light microscopy. A rich algal flora was found: 90 species and 1 variety of 65 genera from Cyanoprokaryota/Cyanobacteria (29 species, 13 genera), Chlorophyta (40 species and 1 variety, 38 genera), Streptophyta (5 species, 1 genus), and Ochrophyta (16 species, 13 genera). Among them were the globally rare Pseudodictyochloris multinucleata (Chlorophyta), found for the first time in such lowland and warm habitats, and Scotiella tuberculata (Chlorophyta), for which this is the first finding in the country. Three of the recorded species are conservationally important. The low floristic similarity between the sites (0-33%) shows the diversity of the algal flora, with no common species found for all the megaliths studied. The most widespread were the strongly adaptive and competitive Stichococcus bacillaris, Apatococcus lobatus, and Chloroidium ellipsoidium (Chlorophyta). The correlations estimated between the species number and substrate temperature (18.1-49.6 °C) suggest the prospect of future research related to the impact of global warming. In addition, the study points to the safety aspects as it revealed species from nine potentially toxin-producing cyanoprokaryotic genera that could be harmful to visitors' health.

Bee-Inspired Healing: Apitherapy in Veterinary Medicine for Maintenance and Improvement Animal Health and Well-Being.

This review aims to present current knowledge on the effects of honey bee products on animals based on in vivo studies, focusing on their application in clinical veterinary practice. Honey's best-proven effectiveness is in treating wounds, including those infected with antibiotic-resistant microorganisms, as evidenced in horses, cats, dogs, mice, and rats. Propolis manifested a healing effect in numerous inflammatory and painful conditions in mice, rats, dogs, and pigs and also helped in oncological cases in mice and rats. Bee venom is best known for its effectiveness in treating neuropathy and arthritis, as shown in dogs, mice, and rats. Besides, bee venom improved reproductive performance, immune response, and general health in rabbits, chickens, and pigs. Pollen was effective in stimulating growth and improving intestinal microflora in chickens. Royal jelly might be used in the management of animal reproduction due to its efficiency in improving fertility, as shown in rats, rabbits, and mice. Drone larvae are primarily valued for their androgenic effects and stimulation of reproductive function, as evidenced in sheep, chickens, pigs, and rats. Further research is warranted to determine the dose and method of application of honey bee products in animals.

Adaptive Sensing Data Augmentation for Drones Using Attention-Based GAN.

Drones have become essential tools across various industries due to their ability to provide real-time data and perform automated tasks. However, integrating multiple sensors on a single drone poses challenges such as payload limitations and data management issues. This paper proposes a comprehensive system that leverages advanced deep learning techniques, specifically an attention-based generative adversarial network (GAN), to address data scarcity in drone-collected time-series sensor data. By adjusting sensing frequency based on operational conditions while maintaining data resolution, our system ensures consistent and high-quality data collection. The spatiotemporal The attention mechanism within the GAN enhances the generation of synthetic data, filling gaps caused by reduced sensing frequency with realistic data. This approach improves the efficiency and performance of various applications, such as precision agriculture, environmental monitoring, and surveillance. The experimental results demonstrated the effectiveness of our methodology in extending the operational range and duration of drones and providing reliable augmented data utilizing a variety of evaluation metrics. Furthermore, the superior performance of the proposed system was verified by comparing it with various comparative GAN models.

Directional swimming patterns in jellyfish aggregations.

Having a profound influence on marine and coastal environments worldwide, jellyfish hold significant scientific, economic, and public interest.1,2,3,4,5 The predictability of outbreaks and dispersion of jellyfish is limited by a fundamental gap in our understanding of their movement. Although there is evidence that jellyfish may actively affect their position,6,7,8,9,10 the role of active swimming in controlling jellyfish movement, and the characteristics of jellyfish swimming behavior, are not well understood. Consequently, jellyfish are often regarded as passively drifting or randomly moving organisms, both conceptually2,11 and in process studies.12,13,14 Here we show that the movement of jellyfish is modulated by distinctly directional swimming patterns that are oriented away from the coast and against the direction of surface gravity waves. Taking a Lagrangian viewpoint from drone videos that allows the tracking of multiple adjacent jellyfish, and focusing on the scyphozoan jellyfish Rhopilema nomadica as a model organism, we show that the behavior of individual jellyfish translates into a synchronized directional swimming of the aggregation as a whole. Numerical simulations show that this counter-wave swimming behavior results in biased correlated random-walk movement patterns that reduce the risk of stranding, thus providing jellyfish with an adaptive advantage critical to their survival. Our results emphasize the importance of active swimming in regulating jellyfish movement and open the way for a more accurate representation in model studies, thus improving the predictability of jellyfish outbreaks and their dispersion and contributing to our ability to mitigate their possible impact on coastal infrastructure and populations.

Analyzing unmanned aerial vehicle (drone) attacks; a disaster medicine perspective.

INTRODUCTION: Unmanned aerial vehicles (UAVs), more commonly known as drones, have rapidly become more diverse in capabilities and utilization through technology advancements and affordability. While drones have had significant positive impact on healthcare and consumer delivery particularly in remote and austere environments, Violent Non-State Actors (VNSAs) have increasingly used drones as weapons in planning and executing terrorist attacks resulting in significant morbidity and mortality. We aim to analyze drone-related attacks globally against civilians and critical infrastructure for more effective hospital and prehospital care preparedness. METHODS: We retrospectively reviewed the Global Terrorism Database (GTD) from 1970 to 2020 to analyze the worldwide prevalence of drone-related attacks against civilians and critical infrastructure. Cases were excluded if they had insufficient information regarding a drone involvement, and if attacks were conducted by the government entities. The trends in the number of attacks per month, as well as the number of fatalities and injuries, were examined using time series and trend analysis. RESULTS: The database search yielded 253 drone-related incidents, 173 of which met inclusion criteria. These incidents resulted in 92 fatalities and 215 injuries with civilian targets most commonly attacked by drones (76 events, 43.9%), followed by military (46 events, 26.5-%). The Middle East region was most affected (168 events, 97% of attacks) and the Islamic state of Iraq was the most common perpetrator (106 events, 61.2%). Almost all attacks were by explosive devices attached to drones (172 events, 99.4%). Time series with linear trend analyses suggested an upward trends of drone attacks by VNSAs, resulting in a greater number of injuries and fatalities, that became more frequent over the years. CONCLUSIONS: Overtime, there were upward trends of drone attacks, with higher lethality and morbidity. There were more injuries compared to fatalities. Most common region affected was the Middle East, and most common type of weapon employed by drone technology was explosive weapon. Investment in medical personnel training, security, and research is crucial for an effective mass-casualty incident response after the drone attacks.

Real-time conflict risk at signalized intersection using drone video: A random parameters logit model with heterogeneity in means and variances.

Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.

Dispatcher nurses' experiences of handling drones equipped with automated external defibrillators in suspected out-of-hospital cardiac arrest - a qualitative study.

BACKGROUND: Reducing the time to treatment by means of cardiopulmonary resuscitation (CPR) and defibrillation is essential to increasing survival after cardiac arrest. A novel method of dispatching drones for delivery of automated external defibrillators (AEDs) to the site of a suspected out-of-hospital cardiac arrest (OHCA) has been shown to be feasible, with the potential to shorten response times compared with the emergency medical services. However, little is known of dispatchers' experiences of using this novel methodology. METHODS: A qualitative semi-structured interview study with a phenomenological approach was used. Ten registered nurses employed at an emergency medical dispatch centre in Gothenburg, Sweden, were interviewed and the data was analysed by qualitative content analysis. The purpose was to explore dispatcher nurses' experiences of deliveries of AEDs by drones in cases of suspected OHCA. RESULTS: Three categories were formed. Nurses expressed varying compliance to the telephone-assisted protocol for dispatch of AED-equipped drones. They experienced uncertainty as to how long would be an acceptable interruption from the CPR protocol in order to retrieve a drone-delivered AED. The majority experienced that collegial support was important. Technical support, routines and training need to be improved to further optimise action in cases of drone-delivered AEDs handled by dispatcher nurses. CONCLUSIONS: Although telephone-assisted routines for drone dispatch in cases of OHCA were available, their use was rare. Registered nurses showed variable degrees of understanding of how to comply with these protocols. Collegial and technical support was considered important, alongside routines and training, which need to be improved to further support bystander use of drone-delivered AEDs. As the possibilities of using drones to deliver AEDs in cases of OHCA are explored more extensively globally, there is a good possibility that this study could be of benefit to other nations implementing similar methods. We present concrete aspects that are important to take into consideration when implementing this kind of methodology at dispatch centres.

Exploring the effect of human-drone communication modality on safety and balance control in virtual construction environments.

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers' safety and balance control within virtual environments. Utilising virtual reality (VR) simulations, the study explored how gesture and speech-based communications influence workers' physical postures and balance, contrasting these modalities with a non-interactive control group. One hundred participants were recruited, and their movements and balance control were tracked using motion sensors while they interacted with virtual drones through either gesture, speech, or without communication. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls, suggesting that advanced HDI can enhance safety on construction sites. However, speech-based interaction increased cognitive workload, highlighting a trade-off between physical safety and mental strain. These findings underscore the potential of integrating intuitive communication methods into construction operations, although further research is needed to optimise these interactions for long-term use and in diverse noise environments.

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers' safety and balance control within virtual environments with a human subject experiment. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls.

Associations between mental health symptoms, trauma, quality of life and coping in adults living in Ukraine: A cross-sectional study a year after the 2022 Russian invasion.

We aimed to assess the mental health of adults living in Ukraine one year after onset of the Russo-Ukrainian war, along with quality of life and coping strategies. Quota sampling was used to collect online survey data from 2364 adults aged 18-79 years living in Ukraine from April 5, 2023 to May 15, 2023. Among adults living in Ukraine, 14.4 % had probable post-traumatic stress disorder (PTSD), another 8.9 % had complex PTSD (CPTSD), 44.2 % had probable depressive disorder, 23.1 % had anxiety disorder and 38.6 % showed significant loneliness. In adjusted models, the number of trauma events experienced during the war showed a dose-response association with PTSD/CPTSD and was associated with depressive disorder and anxiety disorder. Quality of life domains, particularly physical quality of life, were negatively associated with PTSD/CPTSD, depressive disorder, anxiety disorder, and number of trauma events. Maladaptive coping was positively associated with depressive disorder, anxiety disorder, PTSD/CPTSD and loneliness. All quality of life domains were positively associated with using adaptive coping strategies. Mental health disorders are highly prevalent in adults living in Ukraine one year into the war. Policy and services can promote adaptive coping strategies to improve mental health and quality of life for increased resilience during war.

A Novel Approach to Detect Drones Using Deep Convolutional Neural Network Architecture.

Over the past decades, drones have become more attainable by the public due to their widespread availability at affordable prices. Nevertheless, this situation sparks serious concerns in both the cyber and physical security domains, as drones can be employed for malicious activities with public safety threats. However, detecting drones instantly and efficiently is a very difficult task due to their tiny size and swift flights. This paper presents a novel drone detection method using deep convolutional learning and deep transfer learning. The proposed algorithm employs a new feature extraction network, which is added to the modified YOU ONLY LOOK ONCE version2 (YOLOv2) network. The feature extraction model uses bypass connections to learn features from the training sets and solves the "vanishing gradient" problem caused by the increasing depth of the network. The structure of YOLOv2 is modified by replacing the rectified linear unit (relu) with a leaky-relu activation function and adding an extra convolutional layer with a stride of 2 to improve the small object detection accuracy. Using leaky-relu solves the "dying relu" problem. The additional convolution layer with a stride of 2 reduces the spatial dimensions of the feature maps and helps the network to focus on larger contextual information while still preserving the ability to detect small objects. The model is trained with a custom dataset that contains various types of drones, airplanes, birds, and helicopters under various weather conditions. The proposed model demonstrates a notable performance, achieving an accuracy of 77% on the test images with only 5 million learnable parameters in contrast to the Darknet53 + YOLOv3 model, which exhibits a 54% accuracy on the same test set despite employing 62 million learnable parameters.

Effects of UAS Rotor Wash on Air Quality Measurements.

Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments on a UAS body and on a vertical or horizontal extension arm beyond the rotors. For 1 Hz temporal concentration comparisons, correlations of body versus arm placement for the PM2.5 particle sensors yielded R2 = 0.85 and for both gas sensor pairs exceeded R2 of 0.90. Increasing the timestep to 10 s average concentrations throughout the burns improved the R2 value for the PM2.5 to 0.95 from 0.85. Finally, comparison of whole-test average concentrations further increased the correlations between body- and arm-mounted sensors, exceeding R2 of 0.98 for both gases and particle measurements. Evaluation of PM2.5 emission factors with single factor ANOVA analyses showed no significant differences between the values derived from the arm, either vertical or horizontal, and those from the body. These results suggest that rotor wash effects on body- and arm-mounted sensors are minimal in scenarios where short duration, time-averaged concentrations are used to calculate emission factors and whole-area flux values.

UAV telephotography elucidates floristic variability and beta diversity of island cliffs under grazing interventions.

Cliffs contain one of the least known plant communities, which has been overlooked in biodiversity assessments due to the inherent inaccessibility. Our study adopted the unmanned aerial vehicle (UAV) with the telephoto camera to remotely clarify floristic variability across unreachable cliffs. Studied cliffs comprised 17 coastal and 13 inland cliffs in Gageodo of South Korea, among which 9 and 5 cliffs were grazed by the introduced cliff-dwelling goats. The UAV telephotography showed 154 and 166 plant species from coastal and inland cliffs, respectively. Inland cliffs contained more vascular plant species (P < 0.001), increased proportions of fern and woody species (P < 0.05), and decreased proportion of herbaceous species (P < 0.001) than coastal cliffs. It was also found that coastal and inland cliffs differed in the species composition (P < 0.001) rather than taxonomic beta diversity (P = 0.29). Furthermore, grazed coastal cliffs featured the elevated proportions of alien and annual herb species than ungrazed coastal cliffs (P < 0.05). This suggests that coastal cliffs might not be totally immune to grazing if the introduced herbivores are able to access cliff microhabitats; therefore, such anthropogenic introduction of cliff-dwelling herbivores should be excluded to conserve the native cliff plant communities.

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning.

Introduction: Growing grass-legume mixtures for forage production improves both yield productivity and nutritional quality, while also benefiting the environment by promoting species biodiversity and enhancing soil fertility (through nitrogen fixation). Consequently, assessing legume proportions in grass-legume mixed swards is essential for breeding and cultivation. This study introduces an approach for automated classification and mapping of species in mixed grass-clover swards using object-based image analysis (OBIA). Methods: The OBIA procedure was established for both RGB and ten band multispectral (MS) images capturedby an unmanned aerial vehicle (UAV). The workflow integrated structural (canopy heights) and spectral variables (bands, vegetation indices) along with a machine learning algorithm (Random Forest) to perform image segmentation and classification. Spatial k-fold cross-validation was employed to assess accuracy. Results and discussion: Results demonstrated good performance, achieving an overall accuracy of approximately 70%, for both RGB and MS-based imagery, with grass and clover classes yielding similar F1 scores, exceeding 0.7 values. The effectiveness of the OBIA procedure and classification was examined by analyzing correlations between predicted clover fractions and dry matter yield (DMY) proportions. This quantification revealed a positive and strong relationship, with R2 values exceeding 0.8 for RGB and MS-based classification outcomes. This indicates the potential of estimating (relative) clover coverage, which could assist breeders but also farmers in a precision agriculture context.