Search Results (45)

Background/Objectives: With the acceleration of global industrialization and continuous population growth, the world is increasingly confronted with the dual challenges of food insecurity and cultivated land contamination. The screening and breeding of rice varieties with superior agronomic traits and low heavy metal accumulation have therefore become important strategies for ensuring food safety and sustainable agricultural production. Methods: In this study, rice varieties carrying the Gn1a-i gene and exhibiting specific cadmium (Cd) accumulation characteristics were screened using a combination of molecular marker detection and cadmium accumulation evaluation. Specific loop-mediated isothermal amplification (LAMP) primers targeting the Gn1a-i gene were designed and combined with a lateral flow dipstick (LFD) assay to enable rapid genetic screening of rice varieties. A six-day hydroponic experiment under cadmium stress was conducted across three temperature ranges (15–20 °C, 22–27 °C, and 30–35 °C), and cadmium accumulation in different plant organs (roots, stem sheath, and leaves) was analyzed. Results: Seven varieties carrying the Gn1a-i gene, including Xiangwanxian 12, were identified among ten tested rice varieties. Xiangwanxian 12 was subsequently selected for further evaluation, with the high-cadmium-accumulating variety Yuzhenxiang used as a control. At 144 h, the total Cd content in the measured organs of Xiangwanxian 12 was 9.6%, 4.0%, and 23.2% lower than that of Yuzhenxiang under low, medium, and high temperatures, respectively (one-tailed t-test, p < 0.01 for all three temperatures). Conclusions: The integration of LAMP-based genotyping and physiological evaluation provides a novel and reliable strategy for identifying low-Cd rice germplasm. Xiangwanxian 12, which carries the Gn1a-i allele and exhibits consistently lower Cd accumulation than Yuzhenxiang, suggests potential as a candidate for breeding high-yield, low-Cd rice cultivars. Full article

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

The Carpathian Basin is a coherent biogeographic unit whose wildlife populations and pathogen dynamics are increasingly reshaped by administratively fragmented governance, land-use change and linear infrastructure. This review synthesizes evidence that the permeability patterns governing host movement also structure the transboundary exchange of genes and infections, creating a connectivity substrate for conservation genetics and One Health risk. Focusing on wild boar (Sus scrofa), red deer (Cervus elaphus), roe deer (Capreolus capreolus), fallow deer (Dama dama) and the expanding golden jackal (Canis aureus), we integrate population genetic inferences with wildlife epidemiology to examine how highways, border fences and asymmetric management (e.g., supplemental, feeding practices, hunting pressure and surveillance regimes) can generate biological asymmetries across boundaries. We highlight African swine fever as an emblematic disturbance in wild boar populations, discuss cervid risks including tick-borne pathogens and chronic wasting disease (CWD) preparedness and evaluate zoonotic threats associated with carnivore expansion (e.g., Echinococcus spp.). We propose a Carpathian Basin-level monitoring and data-sharing architecture, coupling harmonized passive surveillance, strategic active surveillance for priority pathogens, and standardized genetic marker panels supported by interoperable metadata. A Basin-scale One Health approach is a pragmatic prerequisite for the coordinated prevention, early detection and resilient management of cross-border epizootics and zoonotic threats. Full article

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique for neurological research and therapy, but its effectiveness depends on accurate and stable coil placement. Manual localization based on anatomical landmarks is time-consuming and operator-dependent, while state-of-the-art robotic and neuronavigation systems achieve high accuracy using optical tracking with head-mounted markers and infrared cameras, at the cost of increased system complexity and setup burden. This study presents a cost-effective, markerless robotic-assisted TMS system that combines a 3D depth camera and textile capacitive sensors to assist coil localization and contact control. Facial landmarks detected by the depth camera are used to estimate the motor cortex (C3) location without external tracking markers, while a dual textile-sensor suspension provides compliant “soft-landing” behavior, contact confirmation, and coil-tilt estimation. Experimental evaluation with five participants showed reliable C3 targeting with valid motor evoked potentials (MEPs) obtained in most trials after initial calibration, and tilt-verification experiments revealed that peak MEP amplitudes occurred near balanced sensor readings in 12 of 15 trials (80%). The system employs a collaborative robot designed in accordance with international human–robot interaction safety standards, including force-limited actuation and monitored stopping. These results suggest that the proposed approach can improve the accessibility, safety, and consistency of TMS procedures while avoiding the complexity of conventional optical tracking systems. Full article

This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article

The autonomous landing of unmanned aerial vehicles (UAVs) on moving platforms has potential applications across various domains. However, robust landing remains challenging because the detection reliability of UAVs decreases when the UAV is close to a moving platform. To address this issue, this paper proposes a novel landing strategy that ensures a high detection rate. First, a robust detectable region was established by considering the sensing range and maneuverability limitations of the UAV. Second, a vector field was designed to guide the UAV to the moving platform while remaining in a robust detectable region. Next, safe and accurate landings were achieved by considering the current velocity and vector field. The landing strategy was validated through outdoor flight experiments. A quadrotor equipped with a gimbal-mounted camera was used, and a fractal marker was attached to the moving platform for detection and tracking. When the moving platform moved at a speed of 2–4.3 m/s, the UAV successfully landed on the platform with a distance error of 0.4 m. Because of the robust detectable region and vector field, the detection was conducted with a high success rate (94.9%). Full article

Background/Objective: Salinity is a growing problem affecting a large portion of global agricultural land, particularly in areas where water resources are scarce. The objective of this study was to provide physiological and molecular information on salt-tolerant citrus rootstocks to mitigate the detrimental effects of salinity on citriculture. Methods: Ten accessions belonging to eight Citrus species and four to Poncirus trifoliata Raf. were tested for salinity tolerance (0 and 15 mM NaCl for 1 year) in terms of vegetative and Cl⁻ tissue distribution traits. In addition, most accessions were evaluated for leaf Na⁺ and other cations. Results: All salt tolerant accessions tended to restrict the leaf Cl⁻ content, although in a lower degree than the Cleopatra mandarin. However, differences in their ability to restrict leaf [Na⁺] were evident, contributing to a classification of trifoliate and sour orange accessions that matched their genotypic grouping based on allele sharing at a marker targeting candidate gene coding for the NPF5.9 transporter within LCL-6 quantitative trait locus. Conclusions: Our markers targeting LCl-6 candidate genes coding for NPF5.9, PIP2.1, and CHX20 (citrus GmSALT3 ortholog) could be efficient tools for managing the detected salt tolerance diversity in terms of both Cl⁻ and Na⁺ homeostasis in rootstock breeding programs derived from these species, in addition to Citrus reshni. Full article

Unmanned Surface Vehicles (USVs) are commonly used as mobile docking stations for Unmanned Aerial Vehicles (UAVs) to ensure sustained operational capabilities. Conventional vision-based techniques based on horizontally-placed fiducial markers for autonomous landing are not only susceptible to interference from lighting and shadows but are also restricted by the limited Field of View (FOV) of the visual system. This study proposes a method that integrates an improved minimum snap trajectory planning algorithm with an event-triggered vision-based technique to achieve autonomous landing on a small USV. The trajectory planning algorithm ensures trajectory smoothness and controls deviations from the target flight path, enabling the UAV to approach the USV despite the visual system’s limited FOV. To avoid direct contact between the UAV and the fiducial marker while mitigating the interference from lighting and shadows on the marker, a landing platform with a vertically placed fiducial marker is designed to separate the UAV landing area from the fiducial marker detection region. Additionally, an event-triggered mechanism is used to limit excessive yaw angle adjustment of the UAV to improve its autonomous landing efficiency and stability. Experiments conducted in both terrestrial and river environments demonstrate that the UAV can successfully perform autonomous landing on a small USV in both stationary and moving scenarios. Full article

Wheat exhibits moderate tolerance to salinity. The increasing salinization of arable land poses a significant risk to future wheat production. Therefore, it is imperative to expedite the genetic breeding of wheat for enhanced salt tolerance. This study investigates the genetic and phenotypic diversity of 90 wheat varieties under salt stress, utilizing a comprehensive approach involving trait distribution analysis, hierarchical clustering, kinship estimation, and low-density association analysis. The phenotypic analysis of key agronomic traits revealed significant variability in traits such as leaf area index, canopy temperature, grain area, dry weight, harvest index, grain yield, and tiller number. Most traits exhibited a near-normal distribution, with a few parameters showing skewed or bimodal distributions, indicating the presence of subpopulations with distinct trait profiles. The hierarchical clustering analysis identified five distinct genetic clusters among the wheat varieties, highlighting the complex genetic relationships and variations in salt stress tolerance. Kinship estimates further confirmed the presence of genetic divergence among the accessions, with a majority showing weak or null relationships. Statistical models for association analysis revealed the effectiveness of the Generalized Linear Mixed Model (GLMM) in detecting a greater number of significant genetic markers associated with key agronomic traits, with the GLMM explaining a higher proportion of phenotypic variation. The findings underline the importance of genetic diversity in wheat breeding programs aimed at improving salt stress tolerance and agronomic performance. These results provide valuable insights for future breeding strategies, focusing on the optimization of key traits and marker-assisted selection for the development of salt-tolerant wheat cultivars. Full article

In this paper, an autonomous landing guidance strategy is proposed for quad-UAVs, including landing marker detection, altitude estimation, and adaptive landing commands generation. A double-layered nested marker is designed to ensure that the marker can be captured both in high and low altitudes. A deep learning-based marker detection method is designed where the intersection of union is replaced by the normalized Wasserstein distance in the computation of non-maximum suppression to improve the detection accuracy. The UAV altitude measured by inertial measurement unit is fused with vision-based altitude estimation data to improve the accuracy during the landing process. An image-based visual servoing method is designed to guide the UAV approach to the landing marker. Both simulation and flight experiments are conducted to verify the proposed strategy. Full article

This paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equalization in the HSV color space, to enhance UAV recognition and the detection of markers under shadow conditions. The system incorporates a Kalman filter-based target motion state estimation method and a binocular vision-based depth camera target height estimation method to achieve precise positioning. To tackle the problem of poor controller performance affecting UAV tracking and landing accuracy, a feedforward model predictive control (MPC) algorithm is integrated into a mobile landing control method. This enables the reliable tracking of both stationary and moving targets via the UAV. Additionally, with a consideration of the complexities of real-world flight environments, a mobile tracking and landing control strategy based on airspace division is proposed, significantly enhancing the success rate and safety of UAV mobile landings. The experimental results demonstrate a 100% target recognition success rate and high positioning accuracy, with x and y-axis errors not exceeding 0.01 m in close range, the x-axis relative error not exceeding 0.05 m, and the y-axis error not exceeding 0.03 m in the medium range. In long-range situations, the relative errors for both axes do not exceed 0.05 m. Regarding tracking accuracy, both KF and EKF exhibit good following performance with small steady-state errors when the target is stationary. Under dynamic conditions, EKF outperforms KF with better estimation results and a faster tracking speed. The landing accuracy is within 0.1 m, and the proposed method successfully accomplishes the mobile energy supply mission for the vehicle-mounted UAV system. Full article

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker’s center point relative to the MAV’s body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs. Full article

The utilization of saline land is a global challenge, and cultivating salt-tolerant soybean varieties is beneficial for improving the efficiency of saline land utilization. Exploring the genetic basis of salt-tolerant soybean varieties and developing salt-tolerant molecular markers can effectively promote the process of soybean salt-tolerant breeding. In the study, the membership function method was used to evaluate seven traits related to salt tolerance and comprehensive salt tolerance at the soybean seedling stage; genome-wide association analysis (GWAS) was performed in a natural population containing 200 soybean materials; and linkage analysis was performed in 112 recombinant inbred lines (RIL) population to detect quantitative trait loci (QTLs) of salt tolerance. In the GWAS, 147 SNPs were mapped, explaining 5.28–17.16% of phenotypic variation. In the linkage analysis, 10 QTLs were identified, which could explain 6.9–16.16% of phenotypic variation. And it was found that there were two co-located regions between the natural population and the RIL population, containing seven candidate genes of salt tolerance in soybean. In addition, one colocalization interval was found to contain qZJS-15-1, rs47665107, and rs4793412, all of which could explain more than 10% of phenotypic variation rates, making it suitable for molecular marker development. The physical positions of rs47665107 and rs47934112 were included in qZJS-15-1. Therefore, a KASP marker was designed and developed using Chr. 15:47907445, which was closely linked to the qZJS-15-1. This marker could accurately and clearly cluster the materials of salt-tolerant genotypes in the heterozygous population tested. The QTLs and KASP markers found in the study provide a theoretical and technical basis for accelerating the salt-tolerant breeding of soybean. Full article

Tortula atrovirens (Sm.) Lindb. is an important component of biological soil crusts and possesses an extraordinary tolerance against desiccation in dryland habitats. However, knowledge of the organelle genome of this desiccation-tolerant (DT) moss is still lacking. Here, we assembled the first reported Tortula organelle genome and conducted a comprehensive analysis within the Pottiaceae family. T. atrovirens exhibited the second largest chloroplast genome (129,646 bp) within the Pottiaceae, whereas its mitogenome (105,877 bp) and those of other mosses were smaller in size compared to other land plants. The chloroplast and mitochondrial genomes of T. atrovirens were characterized by the expansion of IR boundaries and the absence of homologous recombination-mediated by large repeats. A total of 57 RNA editing sites were detected through mapping RNA-seq data. Moreover, the gene content and order were highly conserved among the Pottiaceae organelle genomes. Phylogenetic analysis showed that bryophytes are paraphyletic, with their three lineages (hornworts, mosses, and liverworts) and vascular plants forming successive sister clades. Timmiella anomala is clearly separated from the monophyletic Pottiaceae, and T. atrovirens is closely related to Syntrichia filaris within the Pottioideae. In addition, we detected four hypervariable regions for candidate-molecular markers. Our findings provide valuable insights into the organelle genomes of T. atrovirens and the evolutionary relationships within the Pottiaceae family, facilitating future discovery of DT genetic resources from bryophytes. Full article