Search Results (351)

Efficient phenotyping is essential for accelerating genetic improvement in turfgrass breeding, where manual measurements are labor-intensive. This study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes. HSI data (400–1000 nm) were processed through a user-in-the-loop hybrid segmentation pipeline integrating UMAP dimensionality reduction, DBSCAN clustering, Random Forest classification, and pseudo-RGB refinement. To independently assess vegetation classification performance, 10,000 manually annotated reference points from 50 pseudo-RGB images were compared with the automated module, yielding an overall accuracy of 0.9697, a precision of 0.8830, a recall of 0.9240, a specificity of 0.9779, an F1-score of 0.9030, and Cohen’s kappa of 0.8851. A Combined Ranking Score (CRS) integrating five vegetation indices and vegetation pixel count was significantly associated with aerial shoot count (r = −0.445, p < 0.001) and runner count (r = −0.207, p < 0.001). The highest-ranked genotype showed a 9370.3-pixel increase in vegetation area between 6 and 16 weeks after transplanting, compared with 1417.7 pixels for the lowest-ranked genotype. Classification performance declined under high-coverage conditions, indicating increased mixed-pixel ambiguity in dense canopies. These results suggest that HSI-based CRS can support rapid, objective, and non-destructive relative ranking of density-related vegetative growth in turfgrass breeding. Because the study was conducted at a single location and season and correlations with manual traits were moderate, the framework is best interpreted as a screening and ranking tool rather than a direct predictive model. Full article

Background: Operational performance in security-force settings depends on maintaining accurate motor–cognitive and shooting performance under acute physical strain. This descriptive pilot study examined pre–post performance patterns during a fixed-order acute-load protocol and explored whether trial-level and spatial analyses identified changes beyond aggregate scores. Methods: Nineteen applicants (10 men, 9 women; 21.6 ± 1.0 years) completed two testing sequences separated by one week. All participants completed Sequence 1 first and Sequence 2 second; therefore, sequence-related observations were interpreted descriptively rather than causally. In both sequences, participants performed Hawk Eye testing, IPSC-based shooting, and the Jaciak Motor Coordination Test, with the order of Hawk Eye and shooting reversed between sequences. Primary outcomes were first-shot hit rate and Hawk Eye error count. Secondary and exploratory outcomes included shooting miss rate and time, Hawk Eye stimulus time, minimum and maximum response times, trial-level timing, spatial distributions, and cross-task coupling. Results: Heart rate increased markedly after the Jaciak test in both sequences, with end-of-test values corresponding to approximately 86–88% of age-predicted HRmax. Model-based analysis indicated lower post-load odds of a first-shot hit compared with pre-load performance. In contrast, no detectable pre–post change was observed for Hawk Eye error probability. Descriptively, first-shot hit rate decreased in Sequence 1 (62.1 ± 19.9% vs. 42.1 ± 28.2%; p = 0.029), while the decrease in Sequence 2 was smaller and not statistically significant (61.1 ± 24.5% vs. 52.6 ± 28.4%; p = 0.267). Hawk Eye error count showed no statistically detectable pre–post change in either sequence, although maximum response time decreased in Sequence 1 (p = 0.008). Trial-level and spatial analyses indicated additional temporal and location-specific patterns, but exploratory cross-task spatial associations were inconsistent. Conclusions: In this fixed-order descriptive pilot study, post-load testing was associated with lower first-shot shooting performance in this sample, whereas no statistically detectable deterioration was observed for Hawk Eye error probability. However, because the design lacked a no-load control condition and all participants completed the same sequence order, the observed pre-to-post differences cannot be attributed specifically to acute physical load. They should be interpreted as descriptive performance patterns within the implemented protocol. Full article

Background/Objective: Mental fatigue (MF) significantly impairs psychomotor performance in dynamic sports; however, its specific impact on closed-skill precision-demanding tasks remains underexplored. This study investigated the acute effects of experimentally induced MF exposure on psychophysiological responses, mood states, and archery shooting performance. Methods: Fifteen well-trained male compound-bow archers participated in a randomized crossover study. Participants completed an MF condition (30 min modified Stroop task) and a control condition (CON; passive viewing of a neutral documentary), separated by a 72 h washout period. Continuous heart rate (HR), archery shooting accuracy, ratings of perceived exertion (RPE), rating scale of mental effort (RSME), state anxiety (VAS-A), mood states, and exercise enjoyment scale (EES) were assessed. Results: The Stroop task successfully induced subjective MF. Consequently, shooting accuracy significantly deteriorated in the MF condition compared to that in the CON condition (p < 0.001; g = 0.731). While HR and VAS-A remained consistent across conditions, the MF condition elicited a significant increase in RPE (p = 0.007; g = 0.836) and RSME (p = 0.010; g = 0.794). Furthermore, MF significantly increased feelings of anger and fatigue while drastically reducing PACES (p < 0.001; g = 1.530). Conclusions: Acute MF significantly degrades fine motor accuracy in precision sports. The pronounced dissociation between elevated RPE and stable peripheral physiological strain suggests that performance decline is driven by top-down cognitive burden rather than physiological limitations. Therefore, systematic monitoring of cognitive load is crucial for optimizing performance in precision sports. Full article

This systematic review synthesized the existing evidence on neurofeedback interventions applied to football players, aiming to clarify their effects on cognitive, technical–tactical, physical and psychological performance. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, four databases (PubMed, Web of Science, SCOPUS and SportsDiscus) were searched up to November 2025. Seven studies met the inclusion criteria, involving 133 players across youth, amateur, national and elite levels. Neurofeedback protocols primarily targeted alpha or sensorimotor rhythm (SMR) activity, and some were combined with heart rate variability (HRV) biofeedback. Across studies, neurofeedback may be associated with improvements in several cognitive outcomes, including improvements in working memory, visuospatial memory, task switching, mental rotation and decision-making. Limited evidence suggests potential improvements in technical skills (particularly shooting accuracy) and tactical decision-making. Some studies reported changes in physiological markers and stress-recovery capacity, although their interpretation remains uncertain. However, the evidence base remains constrained by small samples, heterogeneous protocols and limited use of randomized controlled designs. Overall, neurofeedback appears to be a potentially promising but still experimental tool to support cognitive and psychophysiological readiness in football, warranting more rigorous and standardized research to establish efficacy and optimal training parameters. Full article

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

Accurate tea-shoot detection in real tea gardens is essential for intelligent harvesting, yet dynamic illumination (low light, strong light, and shadows) can cause brightness/contrast fluctuations and feature distribution shifts, degrading detection stability and localization accuracy. This paper proposes LA-YOLO, a dynamic-light tea-shoot detector based on YOLOv11. First, we construct a dynamic-light benchmark dataset and a difficulty-stratified evaluation protocol with four single-light subsets (A–D) and a mixed-light subset (E). Second, we design LA-CSNorm, an input-side brightness-adaptive preprocessing module that applies gated enhancement to dark samples followed by channel-selective normalization to reduce illumination-induced drift. Third, we propose RECA, a residual efficient channel-attention module to enhance discriminative channels and improve localization stability. Ablation studies show that LA-CSNorm and RECA provide complementary gains, and their combination improves the YOLOv11 baseline to 0.831 mAP@0.5 and 0.621 mAP@0.5:0.95, with only 0.01 M additional parameters. On the mixed-light subset E, LA-YOLO achieves 0.816 mAP@0.5 and 0.613 mAP@0.5:0.95, and consistently outperforms mainstream YOLO variants (e.g., YOLOv11m) under dynamic lighting conditions. These results demonstrate that LA-YOLO offers a robust and deployment-friendly solution for tea-shoot detection in complex natural illumination. Full article

Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper and identify which approach is most suitable under conditions of dynamic biomass partitioning. Non-destructive morphological measurements were collected under greenhouse cultivation, and allometric models based on geometric equations were established as baselines. Their performance was compared with machine learning (ML) models and ensemble learning frameworks. To address limited data availability, numerical data augmentation with Gaussian noise and a variational autoencoder was applied. Among the allometric models, the stick model combined with a sigmoid function showed the highest performance, with an $R^2$ of 0.80 for shoot FW and 0.54 for fruit FW. All ML models outperformed the allometric models, and the ensemble model achieved the highest predictive accuracy, with an $R^2$ of 0.96 for shoot FW and 0.89 for fruit FW. Data augmentation further improved predictive performance across all ML models, particularly for fruit FW prediction. Feature contribution analysis revealed that temporal progression was the dominant predictor of fruit FW, while structural traits played the primary role in shoot FW estimation. Ensemble-based ML, combined with data augmentation, provides a methodological framework for non-destructive FW estimation of sweet pepper in controlled environments such as greenhouses and smart farming systems. Full article

Background/Objectives: Salinity stress limits agricultural production and threatens global food security. Faba bean (Vicia faba L.) is an important legume crop, and identifying salt-stress-responsive genes may support an improvement in salt response. This study aimed to identify intronless genes in faba bean, screen candidate genes associated with salt-stress responses, and develop a KASP marker for salt-response evaluation. Methods: Intronless genes were identified from the faba bean reference genome. Transcriptome analysis was conducted in roots and leaves of two cultivars, Sucan 4 and Yundou 1183, under 150 mM NaCl treatment and control conditions. Candidate genes were examined by expression analysis, functional annotation, PPI prediction, and a luciferase complementation assay. A KASP marker was developed from an SNP within the VfERF1A locus and tested in 97 accessions. Results: A total of 7581 intronless genes were identified, accounting for 20.69% of annotated genes. Fifteen intronless genes were significantly differentially expressed in both roots and leaves of the two cultivars under salt treatment. Functional annotation suggested that VfERF1A and VfHSP17.8 may be involved in salt-stress responses. PPI prediction and the LUC assay provided preliminary support for a possible association of VfERF1A with VfEIN2. The VfERF1A-based KASP marker showed clear genotype clustering, and the two homozygous classes differed significantly in QYmax, relative shoot fresh weight, and relative plant height under salt treatment (p < 0.05). The preliminary predictive accuracy for QYmax was 86.36%. Conclusions: These results provide a genome-wide resource of intronless genes in faba bean, identify candidate genes associated with salt-stress responses, and describe a preliminary KASP marker associated with salt-response traits. Further validation in independent populations, under diverse environmental conditions, and with additional functional evidence is still required. Full article

Woody biomass crops are increasingly considered a promising alternative to conventional agricultural systems due to their potential for sustained carbon sequestration under accelerating climate change. Optimizing management practices in such systems is therefore critical to enhance biomass production and carbon storage. In this study, we investigated how management influences biomass allocation in four poplar plots differing in planting density, variety, and harvest-rotation design during their 6th and 7th year of growth. Biomass stocks were quantified for crown, stem, coarse roots, and fine roots. Management effects were most pronounced in aboveground biomass, whereas belowground responses were less consistent. The highest aboveground biomass was observed in the high-density system within the first rotation (MxHD1), reaching 55.32 Mg ha⁻¹ in 2024 and 94.91 Mg ha⁻¹ in 2025. Belowground biomass ranged from 8.12 to 18.35 Mg ha⁻¹ across plots and years. The root:shoot ratio declined with increasing shoot basal diameter and was highest in the year following harvest. Based on these data, we developed general and management-specific allometric models to predict aboveground and belowground biomass from diameter at breast height. Including management factors improved prediction accuracy, supporting more precise quantification of biomass allocation under different cultivation strategies. Full article

As the low-altitude economy develops rapidly, precise radar altimetry is crucial for ensuring the safety and reliability of drone flights. In the context of urban radio detection, the presence of numerous buildings and ground surfaces gives rise to electromagnetic wave multipath propagation. This objective factor gives rise to errors in radar altimetry. Existing channel models often lack the intricate details required to accurately quantify multipath error mechanisms in kilometer-scale complex electromagnetic environments. Therefore, there is an urgent need for a high-fidelity simulation framework. The present study has put forward a pioneering approach to radio altimetry simulation and accuracy assessment in intricate urban environments. The objective of this study is to investigate the impact of multipath propagation on radar altimetry precision. The present study has proposed a novel integration of radar altimetry simulation with kilometre-scale urban electromagnetic simulation models. The simulation of echo signals has been achieved through the utilization of the shooting and bouncing rays (SBR) method and inverse fast Fourier transform (IFFT). A comparative analysis has been conducted based on ranging results from radar systems for different urban models, thereby enabling a mechanism analysis of factors affecting radar altimetry. The study has demonstrated that increased building density and height, along with reduced elevation angles during altimetry, exacerbate ranging errors. Full article

(1) Background: This study investigated developmental differences in gaze behaviors and performance during basketball free throws among youth athletes. (2) Methods: Forty-six male youth basketball players (U14, U16, U18) each performed 30 standardized free throws while wearing Tobii Pro Glasses 3 to record gaze data (Quiet Eye duration and Total Fixation duration). Shooting accuracy and cognitive workload (NASA-TLX) were also collected. One-way ANOVA, Pearson correlation analysis, and multiple linear regression analysis were conducted to examine age-related differences and the relationships between gaze behavior and performance. (3) Results: Shooting accuracy was moderately correlated with chronological age (r = 0.386, p = 0.010) and training experience (r = 0.367, p = 0.010), and total fixation duration was positively associated with training experience (r = 0.338, p = 0.025). Regression analyses showed that training experience predicted total fixation duration, and both age and experience predicted shooting accuracy when considered separately (p < 0.05), but neither predicted cognitive workload (p > 0.05). Age and training experience were positively associated with shot success. (4) Conclusions: In the youth free-throw task, Quiet Eye duration and total fixation duration were highly correlated but did not independently predict shooting success, while shooting performance was more strongly associated with age and training experience, and perceived cognitive workload remained stable across age groups. Full article

Most 2D human pose estimation frameworks utilize static designs for multi-scale feature fusion, where information from various scales is integrated using fixed weights. A drawback of these approaches is that they often lead to localization biases in complex scenarios. This paper addresses the issues of multi-scale feature mismatch and joint localization biases in pose estimation. From the perspective of feature processing, multi-scale weights must be adapted to the size and position of joints, while joint predictions should adhere to human anatomical constraints. Existing methods lack effective dynamic adaptation, structural constraints, and bidirectional complementarity between high-level semantics and low-level details. They often experience localization biases in occluded scenarios, and the peaks of their heatmaps demonstrate insufficient consistency with the actual positions of the joints. Through theoretical analysis, we identify the causes of performance gaps and propose directions for narrowing them. We propose Bidirectional Multi-Scale Collaborative Pose Estimation (BiMS-Pose), a framework that introduces dynamic weights to adjust feature proportions, establishes bidirectional topological constraints for joint relationships, and integrates a bidirectional attention flow. The framework filters key information from three dimensions, adjusts filtering strategies in real time, and is enhanced by heatmap optimization to improve localization accuracy. Extensive experiments conducted on COCO, MPII, and our self-built Orchard Spraying Pose Dataset (OSPD) demonstrate the effectiveness of BiMS-Pose. In general scenarios, it achieves a significant 1.2 percentage-point increase in average precision (AP) on the COCO val2017 dataset compared to ViTPose while utilizing the same backbone. In agricultural orchard spraying scenarios, it effectively addresses interference factors such as changes in illumination, occlusion, and varying shooting distances, achieving 75.4% average precision (AP) and 90.7% percent of correct keypoints (PCKh@0.5) on the OSPD dataset. Additionally, it maintains an average frame rate of 18.3 FPS on embedded devices, effectively meeting the requirements for real-time monitoring. This highlights the model’s potential for precise, stable, and practical human pose estimation in both general and agricultural application scenarios. Full article

The concrete cracking problem can seriously affect the durability and safety of civil structures. Accurately and quickly measuring the width of concrete cracks can help control defect development in a timely manner. Current research mainly relies on pixel detection of two-dimensional images, which lacks real three-dimensional information about crack lesions. Detection results are also obviously affected by various factors, such as shooting distance and posture, resulting in poor accuracy. Therefore, this paper presents an engineering-integrated solution that combines U-Net-based crack segmentation with binocular vision 3D reconstruction. The focus is placed on the practical deployment of the integrated pipeline, the optimization of key parameters under real inspection conditions, and the experimental validation of measurement accuracy on actual concrete cracks. Firstly, the U-Net deep learning algorithm is used to automatically identify and segment the concrete crack region; then, a binocular vision-based 3D reconstruction pipeline is adopted, and a parallax rejection algorithm based on a “double-threshold” decision is proposed to improve the fidelity of crack disparity maps, and the effect of the filter window size on the concrete crack region is analyzed; finally, an intelligent measurement method based on the 3D reconstruction model is proposed, and the measurement results of concrete crack width can be calculated directly from the 3D reconstruction model. The results show that (1) the model can identify the characteristics of the crack, and the detection effect at 4:00 p.m. is the best, because at this time the light is more uniform with less shadow and moderate contrast between the crack and its background; (2) the reconstruction of the 3D point cloud model of the concrete crack with a filtering window of size 9 × 9 is the best; (3) the maximum error between the calculated and measured values of crack width is 0.31mm, the minimum error is 0.07mm, and the average error is 0.15 mm, which indicates that the measurement accuracy reaches the sub-millimetre level and verifies the validity of the proposed method in this paper. Full article

Objectives: To determine whether four weeks of Rhodiola rosea (RHO) supplementation improves intermittent exercise performance, post-exercise blood lactate concentrations, and decision-making under fatigue in competitive football players. Methods: Twenty-four male competitive football players completed a randomised, double-blind, placebo-controlled 4-week intervention (RHO vs. placebo). Outcomes included Yo-Yo IR2, repeated-sprint ability (RSA), post-RSA blood lactate (0, 3, 5 min), football-specific technical tests (passing and shooting), a video-based decision-making task (reaction time and accuracy), GPS-derived match running metrics, countermovement jump (CMJ), foot tapping (TAP), and haematological markers. Results: Yo-Yo IR2 performance significantly improved in the RHO group (p = 0.012) and was superior to the placebo group (p = 0.046). For RSA, mean sprint time improved significantly from pre- to post-intervention in the RHO group (p = 0.017), whereas no significant change was observed in the placebo group. Post-intervention, mean sprint time was significantly better in RHO than placebo (p = 0.041), with no between-group difference observed at baseline. Best sprint time showed no between-group difference (p = 0.723). Post-exercise blood lactate concentrations were significantly lower in RHO than placebo at 0, 3, and 5 min (all p < 0.05). Under fatigue, the RHO group demonstrated faster reaction time (p = 0.042) and higher decision accuracy (p = 0.049) than placebo. Additionally, the RHO group showed significant pre- to post-intervention improvements in passing and shooting performance (p < 0.05), with between-group differences observed only for short-pass performance. Match total and high-speed running distances were higher in RHO, accompanied by increases in haemoglobin and haematocrit (p < 0.05). Conclusions: Four weeks of Rhodiola rosea supplementation enhanced high-intensity intermittent performance and decision-making under fatigue, with findings suggesting improved performance maintenance rather than increased peak sprint capacity. Full article

Machine vision based on artificial intelligence technology is being actively utilized to reduce defect rates in the photovoltaic module production process. This study aims to propose a machine vision approach using EfficientNet-B0 for defective photovoltaic module detection. In particular, the proposed approach is applied to the electroluminescence (EL) operation, which identifies microcracks in PV modules by using polarization current. The proposed approach extracts low-level structures and local brightness variations, such as busbars, fingers, and cell boundaries, from a single convolutional block. Furthermore, the mobile inverted bottleneck convolution (MBConv) block progressively transforms defect patterns—such as microcracks and dark spots—that appear at various shooting angles into high-level feature representations. The converted image is then processed using global average pooling (GAP), Dropout, and a final fully connected layer (Dense) to calculate the probability of a defective module. A sigmoid activation function is then used to determine whether a PV module is defective. Experiments show that the proposed Efficient-B0-based methodology can stably achieve defect detection accuracy comparable to AlexNet and GoogLeNet, despite its relatively small number of parameters and fast processing speed. Therefore, this study will contribute to increasing the efficiency of EL operation in industrial fields and improving the productivity of PV modules. Full article